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@article{gee1993elastically,
title={Elastically deforming a three-dimensional atlas to match anatomical brain images},
author={Gee, James C and Reivich, Martin and Bajcsy, Ruzena},
journal = {IRCS Technical Reports Series},
number = {192},
year={1993}
}
@article{Heinrich2012,
abstract = {Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this important problem and proposes a modality independent neighbourhood descriptor (MIND) for both linear and deformable multi-modal registration. Based on the similarity of small image patches within one image, it aims to extract the distinctive structure in a local neighbourhood, which is preserved across modalities. The descriptor is based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising. It is able to distinguish between different types of features such as corners, edges and homogeneously textured regions. MIND is robust to the most considerable differences between modalities: non-functional intensity relations, image noise and non-uniform bias fields. The multi-dimensional descriptor can be efficiently computed in a dense fashion across the whole image and provides point-wise local similarity across modalities based on the absolute or squared difference between descriptors, making it applicable for a wide range of transformation models and optimisation algorithms. We use the sum of squared differences of the MIND representations of the images as a similarity metric within a symmetric non-parametric Gauss-Newton registration framework. In principle, MIND would be applicable to the registration of arbitrary modalities. In this work, we apply and validate it for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans. Experimental results show the advantages of MIND over state-of-the-art techniques such as conditional mutual information and entropy images, with respect to clinically annotated landmark locations. {\textcopyright} 2012 Elsevier B.V.},
author = {Heinrich, Mattias P. and Jenkinson, Mark and Bhushan, Manav and Matin, Tahreema and Gleeson, Fergus V. and Brady, Sir Michael and Schnabel, Julia A.},
file = {:home/steffen/Downloads/1-s2.0-S1361841512000643-main.pdf:pdf},
journal = {Medical Image Analysis},
keywords = {Multi-modal similarity metric,Non-local means,Non-rigid registration,Pulmonary images,Self-similarity},
number = {7},
pages = {1423--1435},
pmid = {22722056},
publisher = {Elsevier},
title = {{MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration}},
volume = {16},
year = {2012}
}
@article{Pielawski2020a,
abstract = {We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations). CoMIRs enable the registration of multimodal images where existing registration methods often fail due to a lack of sufficiently similar image structures. CoMIRs reduce the multimodal registration problem to a monomodal one, in which general intensity-based, as well as feature-based, registration algorithms can be applied. The method involves training one neural network per modality on aligned images, using a contrastive loss based on noise-contrastive estimation (InfoNCE). Unlike other contrastive coding methods, used for, e.g., classification, our approach generates image-like representations that contain the information shared between modalities. We introduce a novel, hyperparameter-free modification to InfoNCE, to enforce rotational equivariance of the learnt representations, a property essential to the registration task. We assess the extent of achieved rotational equivariance and the stability of the representations with respect to weight initialization, training set, and hyperparameter settings, on a remote sensing dataset of RGB and near-infrared images. We evaluate the learnt representations through registration of a biomedical dataset of bright-field and second-harmonic generation microscopy images; two modalities with very little apparent correlation. The proposed approach based on CoMIRs significantly outperforms registration of representations created by GAN-based image-to-image translation, as well as a state-of-the-art, application-specific method which takes additional knowledge about the data into account. Code is available at: https://github.com/MIDA-group/CoMIR.},
author = {Pielawski, Nicolas and Wetzer, Elisabeth and {\"{O}}fverstedt, Johan and Lu, Jiahao and W{\"{a}}hlby, Carolina and Lindblad, Joakim and Sladoje, Nata{\v{s}}a},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pielawski et al. - 2020 - CoMIR Contrastive Multimodal Image Representation for Registration(2).pdf:pdf},
journal = {Advances in neural information processing systems},
publisher = {arXiv},
title = {{CoMIR: Contrastive Multimodal Image Representation for Registration}},
year = {2020}
}
@inproceedings{Chen2016,
abstract = {This paper describes InfoGAN, an information-theoretic extension to the Gener-ative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound of the mutual information objective that can be optimized efficiently. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, pres-ence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing supervised methods.},
annote = {Section 4+5 for backround reading on Variational inference + Mutual information

Very good explanation about the intuition of using Mutual information

GAN setting, latent is modelled ad a pair of variables c, z. C encodes MNIST label.
We want to ensure c is used by the generator G(c,z), and not just ignored. We motel a regularization: the mutual information I(c; G(c,z)) shoeld be high.},
archivePrefix = {arXiv},
arxivId = {1606.03657v1},
author = {Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
booktitle = {Advances in Neural Information Processing Systems},
eprint = {1606.03657v1},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chen et al. - Unknown - InfoGAN Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets(2).pdf:pdf},
title = {{InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets}},
year = {2016}
}
@inproceedings{Simonovsky2016,
abstract = {Multimodal registration is a challenging problem due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training,demonstrating good generalization. In this task,we outperform mutual information by a significant margin.},
archivePrefix = {arXiv},
arxivId = {1609.05396},
author = {Simonovsky, Martin and Guti{\'{e}}rrez-Becker, Benjam{\'{i}}n and Mateus, Diana and Navab, Nassir and Komodakis, Nikos},
booktitle = {Lecture Notes in Computer Science},
eprint = {1609.05396},
file = {:home/steffen/Downloads/Simonovsky2016{\_}Chapter{\_}ADeepMetricForMultimodalRegist.pdf:pdf},
pages = {10--18},
publisher = {Springer Verlag},
title = {{A deep metric for multimodal registration}},
volume = {9902},
year = {2016}
}
@article{Blendowski2019,
abstract = {Purpose: Deep convolutional neural networks in their various forms are currently achieving or outperforming state-of-the-art results on several medical imaging tasks. We aim to make these developments available to the so far unsolved task of accurate correspondence finding—especially with regard to image registration. Methods: We propose a two-step hybrid approach to make deep learned features accessible to a discrete optimization-based registration method. In a first step, in order to extract expressive binary local descriptors, we train a deep network architecture on a patch-based landmark retrieval problem as auxiliary task. As second step at runtime within a MRF-regularised dense displacement sampling, their binary nature enables highly efficient similarity computations, thus making them an ideal candidate to replace the so far used handcrafted local feature descriptors during the registration process. Results: We evaluate our approach on finding correspondences between highly non-rigidly deformed lung CT scans from different breathing states. Although the CNN-based descriptors excell at an auxiliary learning task for finding keypoint correspondences, self-similarity-based descriptors yield more accurate registration results. However, a combination of both approaches turns out to generate the most robust features for registration. Conclusion: We present a three-dimensional framework for large lung motion estimation based on the combination of CNN-based and handcrafted descriptors efficiently employed in a discrete registration method. Achieving best results by combining learned and handcrafted features encourages further research in this direction.},
author = {Blendowski, Max and Heinrich, Mattias P.},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Blendowski, Heinrich - 2019 - Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estim.pdf:pdf},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Deep learning,Discrete optimization,Hamming distance,Image registration},
number = {1},
pages = {43--52},
pmid = {30430361},
publisher = {Springer Verlag},
title = {{Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients}},
volume = {14},
year = {2019}
}
@inproceedings{Krebs2017,
abstract = {Robust image registration in medical imaging is essential for comparison or fusion of images, acquired from various perspectives, modalities or at different times. Typically, an objective function needs to be minimized assuming specific a priori deformation models and predefined or learned similarity measures. However, these approaches have difficulties to cope with large deformations or a large variability in appearance. Using modern deep learning (DL) methods with automated feature design, these limitations could be resolved by learning the intrinsic mapping solely from experience. We investigate in this paper how DL could help organ-specific (ROI-specific) deformable registration, to solve motion compensation or atlas-based segmentation problems for instance in prostate diagnosis. An artificial agent is trained to solve the task of non-rigid registration by exploring the parametric space of a statistical deformation model built from training data. Since it is difficult to extract trustworthy ground-truth deformation fields, we present a training scheme with a large number of synthetically deformed image pairs requiring only a small number of real inter-subject pairs. Our approach was tested on inter-subject registration of prostate MR data and reached a median DICE score of.88 in 2-D and.76 in 3-D, therefore showing improved results compared to state-of-the-art registration algorithms.},
annote = {Reinforcement learning-based approach.

Trained on synthetic ground truth deformations of real images.

Does not require a similarity metric, as trained on gt transformations.},
author = {Krebs, Julian and Mansi, Tommaso and Delingette, Herv{\'{e}} and Zhang, Li and Ghesu, Florin C. and Miao, Shun and Maier, Andreas K. and Ayache, Nicholas and Liao, Rui and Kamen, Ali},
booktitle = {Lecture Notes in Computer Science},
file = {:home/steffen/Downloads/Krebs2017{\_}Chapter{\_}RobustNon-rigidRegistrationThr.pdf:pdf},
pages = {344--352},
publisher = {Springer Verlag},
title = {{Robust non-rigid registration through agent-based action learning}},
volume = {10433},
year = {2017}
}
@article{Anthony2020,
abstract = {Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.},
archivePrefix = {arXiv},
arxivId = {2007.03051},
author = {Anthony, Lasse F. Wolff and Kanding, Benjamin and Selvan, Raghavendra},
eprint = {2007.03051},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Anthony, Kanding, Selvan - 2020 - Carbontracker Tracking and Predicting the Carbon Footprint of Training Deep Learning Models.pdf:pdf},
journal = {ICML Workshop on Challenges in Deploying and monitoring Machine Learning Systems},
title = {{Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models}},

year = {2020}
}
@article{ICMJE2015,
author = {ICMJE},
journal = {International Committee of Medical Journal Editors},
title = {{Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals}},
year = {2015}
}
@article{Haskins2019a,
abstract = {Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR–TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR–TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimization strategy. Methods: This work proposes the use of a deep convolutional neural network to learn a similarity metric for MR–TRUS registration. We also use a composite optimization strategy that explores the solution space in order to search for a suitable initialization for the second-order optimization of the learned metric. Further, a multi-pass approach is used in order to smooth the metric for optimization. Results: The learned similarity metric outperforms the classical mutual information and also the state-of-the-art MIND feature-based methods. The results indicate that the overall registration framework has a large capture range. The proposed deep similarity metric-based approach obtained a mean TRE of 3.86 mm (with an initial TRE of 16 mm) for this challenging problem. Conclusion: A similarity metric that is learned using a deep neural network can be used to assess the quality of any given image registration and can be used in conjunction with the aforementioned optimization framework to perform automatic registration that is robust to poor initialization.},
annote = {Description: 
learn a metric-network for similarity judgements

Good:
good results with deep similarity metric on MR-TRUS images

Bad:
requires prior ground truth registration for training
limited to rigid transformations
no alignment of deep features, instead map from two images to similarity},
author = {Haskins, Grant and Kruecker, Jochen and Kruger, Uwe and Xu, Sheng and Pinto, Peter A. and Wood, Brad J. and Yan, Pingkun},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Haskins et al. - 2019 - Learning deep similarity metric for 3D MR–TRUS image registration.pdf:pdf},

journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Convolutional neural networks,Image registration,Image-guided interventions,Multimodal image fusion,Prostate cancer},
number = {3},
pages = {417--425},
pmid = {30382457},
publisher = {Springer Verlag},
title = {{Learning deep similarity metric for 3D MR–TRUS image registration}},

volume = {14},
year = {2019}
}
@article{sotiras2013survey,
author = {Sotiras, Aristeidis and Davatzikos, Christos and Paragios, Nikos},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sotiras, Davatzikos, Paragios - 2013 - Deformable Medical Image Registration A Survey.pdf:pdf},
journal = {IEEE Transactions on Medical Imaging},
number = {7},
pages = {1153--1190},
title = {{Deformable Medical Image Registration: A Survey}},

volume = {32},
year = {2013}
}
@inproceedings{Quay2018,
abstract = {Today, serial block-face scanning electron microscopy (SBF-SEM) is capable of producing teravoxel-scale 3D images of biological structures at nanometer-scale resolutions. Image segmentation is fundamental to data analysis workflows in biological electron microscopy (EM), but SBF-SEM datasets can greatly exceed the manual segmentation capacity of a laboratory. Fast automated segmentation algorithms would alleviate this problem, but practical solutions remain unavailable for many biological problems of interest. Segmentation algorithms using deep neural networks have recently demonstrated significant performance gains, but designing high-performing networks that effectively solve targeted problems remains challenging. We are developing genenet, a Python package to rapidly discover, train, and deploy high-performing neural network architectures for SBF-SEM segmentation with little user intervention. Here, we demonstrate how to use genenet to train an ensemble of segmentation networks for a human platelet tissue sample. Initial results indicate this approach is viable for accelerating the segmentation process.},
annote = {plant-em dataset},
author = {Quay, Matthew and Emam, Zeyad and Anderson, Adam and Leapman, Richard},
booktitle = {International Symposium on Biomedical Imaging},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Quay et al. - 2018 - Designing deep neural networks to automate segmentation for serial block-face electron microscopy.pdf:pdf},


keywords = {Automated segmentation,Deep learning,Electron microscopy,Serial block-face imaging},
pages = {405--408},
publisher = {IEEE Computer Society},
title = {{Designing deep neural networks to automate segmentation for serial block-face electron microscopy}},
volume = {2018-April},
year = {2018}
}
@article{Ashburner2007,
abstract = {This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects. {\textcopyright} 2007 Elsevier Inc. All rights reserved.},
author = {Ashburner, John},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ashburner - 2007 - A fast diffeomorphic image registration algorithm.pdf:pdf},

journal = {NeuroImage},
number = {1},
pages = {95--113},
title = {{A fast diffeomorphic image registration algorithm}},
volume = {38},
year = {2007}
}
@inproceedings{hu2018adversarial,
abstract = {We describe an adversarial learning approach to constrain convolu-tional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted-and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adver-sarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.},
author = {Hu, Yipeng and Gibson, Eli and Ghavami, Nooshin and Bonmati, Ester and Moore, Caroline M and Emberton, Mark and Vercauteren, Tom and Noble, J Alison and Barratt, Dean C},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2018 - Adversarial Deformation Regularization for Training Image Registration Neural Networks.pdf:pdf},
pages = {774--782},
title = {{Adversarial Deformation Regularization for Training Image Registration Neural Networks}},
year = {2018}
}
@article{Castillo2010,
abstract = {A four-dimensional deformable image registration (4D DIR) algorithm, referred to as 4D local trajectory modeling (4DLTM), is presented and applied to thoracic 4D computed tomography (4DCT) image sets. The theoretical framework on which this algorithm is built exploits the incremental continuity present in 4DCT component images to calculate a dense set of parameterized voxel trajectories through space as functions of time. The spatial accuracy of the 4DLTM algorithm is compared with an alternative registration approach in which component phase to phase (CPP) DIR is utilized to determine the full displacement between maximum inhale and exhale images. A publically available DIR reference database (http://www.dir-lab.com) is utilized for the spatial accuracy assessment. The database consists of ten 4DCT image sets and corresponding manually identified landmark points between the maximum phases. A subset of points are propagated through the expiratory 4DCT component images. Cubic polynomials were found to provide sufficient flexibility and spatial accuracy for describing the point trajectories through the expiratory phases. The resulting average spatial error between the maximum phases was 1.25 mm for the 4DLTM and 1.44 mm for the CPP. The 4DLTM method captures the long-range motion between 4DCT extremes with high spatial accuracy. 2010 Institute of Physics and Engineering in Medicine.},
author = {Castillo, Edward and Castillo, Richard and Martinez, Josue and Shenoy, Maithili and Guerrero, Thomas},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Castillo et al. - 2010 - Four-dimensional deformable image registration using trajectory modeling.pdf:pdf},

journal = {Physics in Medicine and Biology},
number = {1},
pages = {305--327},
title = {{Four-dimensional deformable image registration using trajectory modeling}},
volume = {55},
year = {2010}
}
@inproceedings{Kingma2015,
abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
archivePrefix = {arXiv},
arxivId = {1412.6980},
author = {Kingma, Diederik P. and Ba, Jimmy Lei},
booktitle = {International Conference on Learning Representations},
eprint = {1412.6980},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kingma, Ba - 2015 - Adam A method for stochastic optimization.pdf:pdf},
title = {{Adam: A method for stochastic optimization}},
year = {2015}
}
@article{citationrequired,
author = {CitationRequired},
title = {{TODO}},
year = {9999}
}
@inproceedings{Li2010,
abstract = {Topological changes are common in brain MR images for aging or disease studies. For deformable registration algorithms, which are formulated as a variational problem and solved by the minimization of certain energy functional, topological changes can cause false deformation in the resulting vector field, and affect algorithm convergence. In this work, we focus on the effect of topological changes on diffeomorphic and inverse-consistent deformable registration algorithms, specifically, diffeomorphic demons and symmetric LDDMM. We first use a simple example to demonstrate the adverse effect of topological changes on these algorithms. Then, we propose an novel framework that can be imposed onto any existing diffeomorphic and inverse-consistent deformable registration algorithm. Our framework renders these registration algorithms robust to topological changes, where the outputwill consist of two components. The first is a deformation field that presents only the brain structural change which is the expected vector field if the topological change did not exist. The second component is a label map that provides a segmentation of the topological changes appeared in input images. {\textcopyright}2010 IEEE.},
annote = {TLDR: identify topological changes, ignore them during registration

Goal: fully diffeomorphic registration, ignoring topological changes in the process.
Topological changes are identified and removed from the image, allowing registration without being disturbed by them.

multi-step process:
1) coarse-scale registration
2) identify non-matching areas in coarse sale reg, these are the discontenueties
3) replace these areas by interpolating surrounding tissue
4) another normal registration step, now the topological changes are no longer present},
author = {Li, Xiaoxing and Wyatt, Chritopher},
booktitle = {2010 7th IEEE International Symposium on Biomedical Imaging},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Li, Wyatt - 2010 - Modeling topological changes in deformable registration.pdf:pdf},

keywords = {Deformable registration,Segmentation,Topological change},
pages = {360--363},
title = {{Modeling topological changes in deformable registration}},
year = {2010}
}
@article{Parisot2014,
abstract = {In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model. {\textcopyright} 2014 Elsevier B.V..},
author = {Parisot, Sarah and Wells, William and Chemouny, St{\'{e}}phane and Duffau, Hugues and Paragios, Nikos},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Parisot et al. - 2014 - Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs.pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Brain tumors,Concurrent segmentation/registration,Markov Random Fields,Min-marginals},
number = {4},
pages = {647--659},
publisher = {Elsevier},
title = {{Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs}},
volume = {18},
year = {2014}
}
@article{Fu2019,
abstract = {This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain deformable registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.},
annote = {Good review with statistics over the field},
archivePrefix = {arXiv},
arxivId = {1912.12318},
author = {Fu, Yabo and Lei, Yang and Wang, Tonghe and Curran, Walter J. and Liu, Tian and Yang, Xiaofeng},
eprint = {1912.12318},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fu et al. - 2019 - Deep Learning in Medical Image Registration A Review.pdf:pdf},
title = {{Deep Learning in Medical Image Registration: A Review}},

year = {2019}
}
@article{Kwon2014,
abstract = {We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches. {\textcopyright} 1982-2012 IEEE.},
annote = {Registration of tumors to cavities:
+ Uses different loss functions depending on health/unhealth tissue
- uses a whole lot of domain specific knowledge
excludes non-matching areas from the registration, but does not support topology-changing/ inserting new.

Summary:
- register to a probabalistic segmentation atlas
- manually segment tumors/cavities in the atlas:
a) place tumor as a point-annotation, expand with radius
b) expand edema even further around the tumor, masked by white-matter map
- Register the images, using NCC for health regions, L2 loss to match tumor and post-oprtative cavity.},
author = {Kwon, Dongjin and Niethammer, Marc and Akbari, Hamed and Bilello, Michel and Davatzikos, Christos and Pohl, Kilian M.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kwon et al. - 2014 - PORTR Pre-operative and post-recurrence brain tumor registration.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {Brain tumor magnetic resonance imaging (MRI),Deformable registration,Discrete-continuous optimization,Tumor growth model,Tumor segmentation},
number = {3},
pages = {651--667},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{PORTR: Pre-operative and post-recurrence brain tumor registration}},
volume = {33},
year = {2014}
}
@article{Schmidt-Richberg2012,
abstract = {The accurate estimation of respiratory lung motion by non-linear registration is currently an important topic of research and required for many applications in pulmonary image analysis, e.g. for radiotherapy treatment planning. A special challenge for lung registration is the sliding motion between visceral an parietal pleurae during breathing, which causes discontinuities in the motion field. It has been shown that accounting for this physiological aspect by modeling the sliding motion using a direction-dependent regularization approach can significantly improve registration results. While the potential of such physiology-based regularization methods has been demonstrated in several publications, so far only simple explicit solution schemes were applied due to the computational complexity. In this paper, a numerical solution of the direction-dependent regularization based on Fast Explicit Diffusion (FED) is presented. The approach is tested for motion estimation on 23 thoracic CT images and a significant improvement over the classic explicit solution is shown. {\textcopyright} 2012 Springer-Verlag.},
author = {Schmidt-Richberg, Alexander and Ehrhardt, Jan and Werner, Ren{\'{e}} and Handels, Heinz},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Schmidt-Richberg et al. - 2012 - Fast explicit diffusion for registration with direction-dependent regularization.pdf:pdf},


journal = {Biomedical Image Registration},
pages = {220--228},
publisher = {Springer, Berlin, Heidelberg},
title = {{Fast explicit diffusion for registration with direction-dependent regularization}},
volume = {7359},
year = {2012}
}
@techreport{Wilcoxon1945,
annote = {statistical significance test work ranking different models},
author = {Wilcoxon, Frank},
booktitle = {Biometrics Bulletin},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wilcoxon - 1945 - Individual Comparisons by Ranking Methods.pdf:pdf},
number = {6},
pages = {80--83},
title = {{Individual Comparisons by Ranking Methods}},

volume = {1},
year = {1945}
}
@article{Pace2013,
abstract = {We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall. {\textcopyright} 2013 IEEE.},
annote = {Aasa: "an instrumental paper of how we think about sliding motions"},
author = {Pace, Danielle F. and Aylward, Stephen R. and Niethammer, Marc},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Unknown - Unknown - A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {Abdominal computed tomography (CT),deformable image registration,locally adaptive regularization,respiratory motion,sliding motion,thoracic CT},
number = {11},
pages = {2114--2126},
title = {{A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs}},
volume = {32},
year = {2013}
}
@article{christensen1996,
abstract = {A general automatic approach is presented for accommodating local shape variation when mapping a two-dimensional (2-D) or three-dimensional (3-D) template image into alignment with a topologically similar target image. Local shape variability is accommodated by applying a vector-field transformation to the underlying material coordinate system of the template while constraining the transformation to be smooth (globally positive definite Jacobian). Smoothness is guaranteed without specifically penalizing large-magnitude deformations of small subvolumes by constraining the transformation on the basis of a Stokesian limit of the fluid-dynamical Navier-Stokes equations. This differs fundamentally from quadratic penalty methods, such as those based on linearized elasticity or thin-plate splines, in that stress restraining the motion relaxes over time allowing large-magnitude deformations. Kinematic nonlinearities are inherently necessary to maintain continuity of structures during large-magnitude deformations, and are included in all results. After initial global registration, final mappings are obtained by numerically solving a set of nonlinear partial differential equations associated with the constrained optimization problem. Automatic regridding is performed by propagating templates as the nonlinear transformations evaluated on a finite lattice become singular. Application of the method to intersubject registration of neuroanatomical structures illustrates the ability to account for local anatomical variability. {\textcopyright} 1996 IEEE.},
author = {Christensen, Gary E. and Rabbitt, Richard D. and Miller, Michael I.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Christensen, Rabbitt, Miller - 1996 - Deformable templates using large deformation kinematics.pdf:pdf},

journal = {IEEE Transactions on Image Processing},
number = {10},
pages = {1435--1447},
title = {{Deformable templates using large deformation kinematics}},
volume = {5},
year = {1996}
}
@inproceedings{hou2017deep,
author = {Hou, Xianxu and Shen, Linlin and Sun, Ke and Qiu, Guoping},
booktitle = {Winter Conference on Applications of Computer Vision},
organization = {IEEE},
pages = {1133--1141},
title = {{Deep feature consistent variational autoencoder}},
year = {2017}
}
@article{Fu2020,
author = {Fu, Yabo and Lei, Yang and Wang, Tonghe and Higgins, Kristin and Bradley, Jeffrey D. and Curran, Walter J. and Liu, Tian and Yang, Xiaofeng},


journal = {Medical Physics},
publisher = {Wiley},
title = {{LungRegNet: an unsupervised deformable image registration method for 4D‐CT lung}},
year = {2020}
}
@article{pai2016diffeomorphic,
abstract = {In this paper, we propose a multi-scale, multi-kernel shape, compactly supported kernel bundle framework for stationary velocity field-based image registration (Wendland kernel bundle stationary velocity field, wKB-SVF). We exploit the possibility of directly choosing kernels to construct a reproducing kernel Hilbert space (RKHS) instead of imposing it from a differential operator. The proposed framework allows us to minimize computational cost without sacrificing the theoretical foundations of SVF-based diffeomorphic registration. In order to recover deformations occurring at different scales, we use compactly supported Wendland kernels at multiple scales and orders to parameterize the velocity fields, and the framework allows simultaneous optimization over all scales. The performance of wKB-SVF is extensively compared to the 14 non-rigid registration algorithms presented in a recent comparison paper. On both MGH10 and CUMC12 datasets, the accuracy of wKB-SVF is improved when compared to other registration algorithms. In a disease-specific application for intra-subject registration, atrophy scores estimated using the proposed registration scheme separates the diagnostic groups of Alzheimer's and normal controls better than the state-of-the-art segmentation technique. Experimental results show that wKB-SVF is a robust, flexible registration framework that allows theoretically well-founded and computationally efficient multi-scale representation of deformations and is equally well-suited for both inter-and intra-subject image registration.},
annote = {difeomorphic registration has 3 subfields:
1) large deformation diffeomorpic metric mapping (LDDMM)
2) freeform deformation
3) Stationary velocity fields (SVFs)},
author = {Pai, Akshay and Sommer, Stefan and Sorensen, Lauge and Darkner, Sune and Sporring, Jon and Nielsen, Mads},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Pai et al. - 2016 - Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {Kernel bundle framework,registration,reproducing kernel hilbert spaces,wendland kernels},
number = {6},
pages = {1369--1380},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Kernel Bundle Diffeomorphic Image Registration Using Stationary Velocity Fields and Wendland Basis Functions}},
volume = {35},
year = {2016}
}
@inproceedings{Schnabel2001,
abstract = {This work presents a framework for non-rigid registration which extends and generalizes a previously developed technique by Rueckert et al. [1]. We combine multi-resolution optimization with free-form deformations (FFDs) based on multi-level B-splines to simulate a non-uniform control point distribution. We have applied this to a number of different medical registration tasks to demonstrate its wide applicability, including interventional MRI brain tissue deformation compensation, breathing motion compensation in liver MRI, intramodality inter-modality registration of pre-operative brain MRI to CT electrode implant data, and inter-subject registration of brain MRI. Our results demonstrate that the new algorithm can successfully register images with an improved performance, while achieving a significant reduction in run-time.},
author = {Schnabel, Julia A. and Rueckert, Daniel and Quist, Marcel and Blackall, Jane M. and Castellano-Smith, Andy D. and Hartkens, Thomas and Penney, Graeme P. and Hall, Walter A. and Liu, Haiying and Truwit, Charles L. and Gerritsen, Frans A. and Hill, Derek L.G. and Hawkes, David J.},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Schnabel et al. - 2001 - A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations.pdf:pdf},
pages = {573--581},
publisher = {Springer Verlag},
title = {{A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations}},
volume = {2208},
year = {2001}
}
@article{Zhang2018,
abstract = {While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.},
archivePrefix = {arXiv},
arxivId = {1801.03924},
author = {Zhang, Richard and Isola, Phillip and Efros, Alexei A. and Shechtman, Eli and Wang, Oliver},
eprint = {1801.03924},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhang et al. - 2018 - The Unreasonable Effectiveness of Deep Features as a Perceptual Metric.pdf:pdf},
journal = {Conference on Computer Vision and Pattern Recognition},
pages = {586--595},
publisher = {IEEE Computer Society},
title = {{The Unreasonable Effectiveness of Deep Features as a Perceptual Metric}},

year = {2018}
}
@inproceedings{Arsigny2006,
abstract = {In this article, we focus on the computation of statistics of invertible geometrical deformations (i.e., diffeomorphisms), based on the generalization to this type of data of the notion of principal logarithm. Remarkably, this logarithm is a simple 3D vector field, and is well-defined for diffeomorphisms close enough to the identity. This allows to perform vectorial statistics on diffeomorphisms, while preserving the invertibility constraint, contrary to Euclidean statistics on displacement fields. We also present here two efficient algorithms to compute logarithms of diffeomorphisms and exponentials of vector fields, whose accuracy is studied on synthetic data. Finally, we apply these tools to compute the mean of a set of diffeomorphisms, in the context of a registration experiment between an atlas an a database of 9 T1 MR images of the human brain. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
annote = {Proposes logarithms on geometrical deformations, based on matrix exponentials.

The logarithm of a geometric transformation Phi is given by a simple vector field V, also called Stationary Velocity Field (SVF).

V = log(Phi)
Phi = exp(V)
log(Phi) = V

Calculation methods are given in the paper.

Instead of doing statistics on the diffomorphism (complicated functon), we can simply do statistics on its logarithm V (simple vector field)! Those statistics are invertible back to the diffeomorphism (contrary to just doing statistics on displacement fields)},
author = {Arsigny, Vincent and Commowick, Olivier and Pennec, Xavier and Ayache, Nicholas},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Arsigny et al. - 2006 - A log-euclidean framework for statistics on diffeomorphisms.pdf:pdf},


pages = {924--931},
pmid = {17354979},
publisher = {Springer Verlag},
title = {{A log-euclidean framework for statistics on diffeomorphisms}},
year = {2006}
}
@article{Risser2013a,
abstract = {In this paper, we propose a new strategy for modelling sliding conditions when registering 3D images in a piecewise-diffeomorphic framework. More specifically, our main contribution is the development of a mathematical formalism to perform Large Deformation Diffeomorphic Metric Mapping registration with sliding conditions. We also show how to adapt this formalism to the LogDemons diffeomorphic registration framework. We finally show how to apply this strategy to estimate the respiratory motion between 3D CT pulmonary images. Quantitative tests are performed on 2D and 3D synthetic images, as well as on real 3D lung images from the MICCAI EMPIRE10 challenge. Results show that our strategy estimates accurate mappings of entire 3D thoracic image volumes that exhibit a sliding motion, as opposed to conventional registration methods which are not capable of capturing discontinuous deformations at the thoracic cage boundary. They also show that although the deformations are not smooth across the location of sliding conditions, they are almost always invertible in the whole image domain. This would be helpful for radiotherapy planning and delivery. {\textcopyright} 2012.},
annote = {sliding motions by masking a gaussian-smoothing regularizer},
author = {Risser, Laurent and Vialard, Fran{\c{c}}ois Xavier and Baluwala, Habib Y. and Schnabel, Julia A.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Risser et al. - 2013 - Piecewise-diffeomorphic image registration Application to the motion estimation between 3D CT lung images with(2).pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Diffeomorphic registration,LDDMM,LogDemons,Respiratory motion,Sliding motion},
number = {2},
pages = {182--193},
publisher = {Elsevier},
title = {{Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions}},
volume = {17},
year = {2013}
}
@article{Nielsen2020,
annote = {Topology altering diffeomorphic registration.
Method in three steps:
1) manual finding of discontenueties
2) expansion of discontenueties: We insert a "hole" into the image, and push everything else to the side (but do not hide/remove it)
3) normal registration

Step 2 and 3 are optimized alternatingly. Free parameter in 1) is the size of the expansion, free parametert in 2) is the deformation



Transformation in two steps:
1) introduce and expand topological holes
a) discontinuous expansion in the deformation field
b) changing intensity within the resulting hole
2) diffeomorphic registration

For 1)
alpha channel. a=0 is hole. 0{\textless}a{\textless}1
during superimposition, holes are filled with a background image beta

Expansion of holes:},
author = {Nielsen, Rune Kok and Darkner, Sune and Ferangen, Aasa},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nielsen, Darkner, Ferangen - 2020 - TopAwaRe Topology-Aware Registration (PaMi version).pdf:pdf},
journal = {PaMi},
pages = {1--11},
title = {{TopAwaRe : Topology-Aware Registration (PaMi version)}},
year = {2020}
}
@article{Klein2009,
annote = {Evaluation of 14 classical registration algorithms.

Great overview of Datasets and Algorithms.},
author = {Klein, Arno and Andersson, Jesper and Ardekani, Babak A. and Ashburner, John and Avants, Brian and Chiang, Ming Chang and Christensen, Gary E. and Collins, D. Louis and Gee, James and Hellier, Pierre and Song, Joo Hyun and Jenkinson, Mark and Lepage, Claude and Rueckert, Daniel and Thompson, Paul and Vercauteren, Tom and Woods, Roger P. and Mann, J. John and Parsey, Ramin V.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Klein et al. - 2009 - Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.pdf:pdf},

journal = {NeuroImage},
number = {3},
pages = {786--802},
title = {{Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration}},
volume = {46},
year = {2009}
}
@incollection{Fidon2019,
abstract = {Anatomically plausible image registration often requires volumetric preservation. Previous approaches to incompressible image registration have exploited relaxed constraints, ad hoc optimisation methods or practically intractable computational schemes. Divergence-free velocity fields have been used to achieve incompressibility in the continuous domain, although, after discretisation, no guarantees have been provided. In this paper, we introduce stationary velocity fields (SVFs) parameterised by divergence-conforming B-splines in the context of image registration. We demonstrate that sparse linear constraints on the parameters of such divergence-conforming B-Splines SVFs lead to being exactly divergence-free at any point of the continuous spatial domain. In contrast to previous approaches, our framework can easily take advantage of modern solvers for constrained optimisation, symmetric registration approaches, arbitrary image similarity and additional regularisation terms. We study the numerical incompressibility error for the transformation in the case of an Euler integration, which gives theoretical insights on the improved accuracy error over previous methods. We evaluate the proposed framework using synthetically deformed multimodal brain images, and the STACOM11 myocardial tracking challenge. Accuracy measurements demonstrate that our method compares favourably with state-of-the-art methods whilst achieving volume preservation.},
annote = {Proposes an optimization technique for incompressible image registration (= volumes are preserved). Optimization based on a linear solver (with constraints) to determine a SVF based on b-splines},
archivePrefix = {arXiv},
arxivId = {1907.01593},
author = {Fidon, Lucas and Ebner, Michael and Garcia-Peraza-Herrera, Luis C. and Modat, Marc and Ourselin, S{\'{e}}bastien and Vercauteren, Tom},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1907.01593},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fidon et al. - 2019 - Incompressible Image Registration Using Divergence-Conforming B-Splines.pdf:pdf},


pages = {438--446},
title = {{Incompressible Image Registration Using Divergence-Conforming B-Splines}},
year = {2019}
}
@techreport{Chan,
abstract = {Figure 1: "Do as I Do" motion transfer: given a YouTube clip of a ballerina (top), and a video of a graduate student performing various motions, our method transfers the ballerina's performance onto the student (bottom). Abstract This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer. * C. Chan is currently a graduate student at MIT CSAIL. † T. Zhou is currently affiliated with Humen, Inc.},
annote = {Video to graph to vedea generator

supervised},
archivePrefix = {arXiv},
arxivId = {1808.07371v2},
author = {Chan, Caroline and Ginosar, Shiry and Zhou, Tinghui and Efros, Alexei A},
eprint = {1808.07371v2},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chan et al. - Unknown - Everybody Dance Now.pdf:pdf},
title = {{Everybody Dance Now}}
}
@incollection{Xu2019,
abstract = {Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approach can use existing segmentations when available and computes them via the segmentation network otherwise, thereby providing the same registration benefit. Conversely, segmentation network training benefits from the registration, which essentially provides a realistic form of data augmentation. Experiments on knee and brain 3D magnetic resonance (MR) images show that our approach achieves large simultaneous improvements of segmentation and registration accuracy (over independently trained networks) and allows training high-quality models with very limited training data. Specifically, in a one-shot-scenario (with only one manually labeled image) our approach increases Dice scores ({\%}) over an unsupervised registration network by 2.7 and 1.8 on the knee and brain images respectively.},
annote = {supervised

Train a segmentation and registration network in tandem, based on supervised segmentation data.

weakly supervised setting.

The two networks complement each other:
-Registration of images is a good data augmentation step for image segmentation tasks
- The segmentation network can provide segmentations for weak supervision of the registration network, if no ground truth segmentation is available},
archivePrefix = {arXiv},
arxivId = {1904.08465},
author = {Xu, Zhenlin and Niethammer, Marc},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1904.08465},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Xu, Niethammer - 2019 - DeepAtlas Joint Semi-supervised Learning of Image Registration and Segmentation.pdf:pdf},


pages = {420--429},
title = {{DeepAtlas: Joint Semi-supervised Learning of Image Registration and Segmentation}},
year = {2019}
}
@incollection{Hu2019a,
abstract = {Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned. Current learning-based registration methods have adopted the same paradigm and typically predict, for any new input image pair, dense correspondences in the form of a dense displacement field or parameters of a spatial transformation model. However, in many applications of registration, the spatial transformation itself is only required to propagate points or regions of interest (ROIs). In such cases, detailed pixel- or voxel-level correspondence within or outside of these ROIs often have little clinical value. In this paper, we propose an alternative paradigm in which the location of corresponding image-specific ROIs, defined in one image, within another image is learnt. This results in replacing image registration by a conditional segmentation algorithm, which can build on typical image segmentation networks and their widely-adopted training strategies. Using the registration of 3D MRI and ultrasound images of the prostate as an example to demonstrate this new approach, we report a median target registration error (TRE) of 2.1 mm between the ground-truth ROIs defined on intraoperative ultrasound images and those propagated from the preoperative MR images. Significantly lower ({\textgreater}34pct) TREs were obtained using the proposed conditional segmentation compared with those obtained from a previously-proposed spatial-transformation-predicting registration network trained with the same multiple ROI labels for individual image pairs. We conclude this work by using a quantitative bias-variance analysis to provide one explanation of the observed improvement in registration accuracy.},
annote = {weak supervision (with segmentation data)

instead of learning a dense displacement field conditioned on two images, we propose to learn a segmentation of ROI of one image, conditioned on the segmentation data of another image

very applicable to non-diffeomorphic registration},
archivePrefix = {arXiv},
arxivId = {1907.00438},
author = {Hu, Yipeng and Gibson, Eli and Barratt, Dean C. and Emberton, Mark and Noble, J. Alison and Vercauteren, Tom},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1907.00438},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2019 - Conditional Segmentation in Lieu of Image Registration.pdf:pdf},


pages = {401--409},
title = {{Conditional Segmentation in Lieu of Image Registration}},
year = {2019}
}
@incollection{anandan1993hierarchical,
address = {Boston, MA},
author = {Anandan, P. and Bergen, J. R. and Hanna, K. J. and Hingorani, Rajesh},
booktitle = {Motion Analysis and Image Sequence Processing},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Anandan et al. - 1993 - Hierarchical Model-Based Motion Estimation.pdf:pdf},
pages = {1--22},
publisher = {Springer US},
title = {{Hierarchical Model-Based Motion Estimation}},

year = {1993}
}
@incollection{Lee2019,
annote = {???

registration based on regions of interest and associated features.},
author = {Lee, David S. and Sahib, Ashish and Wade, Benjamin and Narr, Katherine L. and Hellemann, Gerhard and Woods, Roger P. and Joshi, Shantanu H.},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lee et al. - 2019 - Multimodal Data Registration for Brain Structural Association Networks.pdf:pdf},


pages = {373--381},
title = {{Multimodal Data Registration for Brain Structural Association Networks}},
year = {2019}
}
@article{Long2015,
author = {Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Long, Shelhamer, Darrell - 2015 - Fully Convolutional Networks for Semantic Segmentation.pdf:pdf},
journal = {CVPR},
pages = {3431--3440},
title = {{Fully Convolutional Networks for Semantic Segmentation}},
year = {2015}
}
@techreport{Siarohin2019,
abstract = {Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, human bodies), our method can be applied to any object of this class. To achieve this, we decouple appearance and motion information using a self-supervised formulation. To support complex motions, we use a representation consisting of a set of learned keypoints along with their local affine transformations. A generator network models occlusions arising during target motions and combines the appearance extracted from the source image and the motion derived from the driving video. Our framework scores best on diverse benchmarks and on a variety of object categories. Our source code is publicly available 1 .},
annote = {Registration with occlusion mask, into image generation

Can we levearge the occlusion mask for image registration tasks?

Cool end-to-end trained registration+generation network.},
author = {Siarohin, Aliaksandr and Lathuili{\`{e}}re, St{\'{e}}phane and Tulyakov, Sergey and Ricci, Elisa and Sebe, Nicu},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Siarohin et al. - 2019 - First Order Motion Model for Image Animation.pdf:pdf},
pages = {7137--7147},
title = {{First Order Motion Model for Image Animation}},

year = {2019}
}
@article{Risser2013,
abstract = {In this paper, we propose a new strategy for modelling sliding conditions when registering 3D images in a piecewise-diffeomorphic framework. More specifically, our main contribution is the development of a mathematical formalism to perform Large Deformation Diffeomorphic Metric Mapping registration with sliding conditions. We also show how to adapt this formalism to the LogDemons diffeomorphic registration framework. We finally show how to apply this strategy to estimate the respiratory motion between 3D CT pulmonary images. Quantitative tests are performed on 2D and 3D synthetic images, as well as on real 3D lung images from the MICCAI EMPIRE10 challenge. Results show that our strategy estimates accurate mappings of entire 3D thoracic image volumes that exhibit a sliding motion, as opposed to conventional registration methods which are not capable of capturing discontinuous deformations at the thoracic cage boundary. They also show that although the deformations are not smooth across the location of sliding conditions, they are almost always invertible in the whole image domain. This would be helpful for radiotherapy planning and delivery. {\textcopyright} 2012.},
annote = {Aasa: "an instrumental paper of how we think about sliding motions"},
author = {Risser, Laurent and Vialard, Fran{\c{c}}ois Xavier and Baluwala, Habib Y. and Schnabel, Julia A.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Risser et al. - 2013 - Piecewise-diffeomorphic image registration Application to the motion estimation between 3D CT lung images with sl.pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Diffeomorphic registration,LDDMM,LogDemons,Respiratory motion,Sliding motion},
number = {2},
pages = {182--193},
title = {{Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions}},
volume = {17},
year = {2013}
}
@inproceedings{deng2009imagenet,
author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle = {Conference on Computer Vision and Pattern Recognition},
pages = {248--255},
title = {{Imagenet: A large-scale hierarchical image database}},
year = {2009}
}
@article{Ehrhardt2011,
abstract = {Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 $\backslash$pm 1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis. {\textcopyright} 2010 IEEE.},
author = {Ehrhardt, Jan and Werner, Ren{\'{e}} and Schmidt-Richberg, Alexander and Handels, Heinz},


journal = {IEEE Transactions on Medical Imaging},
keywords = {4D computed tomography (CT),Diffeomorphic registration,motion modeling,respiratory motion,statistical atlas generation},
number = {2},
pages = {251--265},
pmid = {20876013},
title = {{Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration}},
volume = {30},
year = {2011}
}
@article{Castillo2009,
abstract = {Expert landmark correspondences are widely reported for evaluating deformable image registration (DIR) spatial accuracy. In this report, we present a framework for objective evaluation of DIR spatial accuracy using large sets of expert-determined landmark point pairs. Large samples (over 1100) of pulmonary landmark point pairs were manually generated for five cases. Estimates of inter- and intra-observer variation were determined from repeated registration. Comparative evaluation of DIR spatial accuracy was performed for two algorithms, a gradient-based optical flow algorithm and a landmark-based moving least-squares algorithm. The uncertainty of spatial error estimates was found to be inversely proportional to the square root of the number of landmark point pairs and directly proportional to the standard deviation of the spatial errors. Using the statistical properties of this data, we performed sample size calculations to estimate the average spatial accuracy of each algorithm with 95pct confidence intervals within a 0.5 mm range. For the optical flow and moving least-squares algorithms, the required sample sizes were 1050 and 36, respectively. Comparative evaluation based on fewer than the required validation landmarks results in misrepresentation of the relative spatial accuracy. This study demonstrates that landmark pairs can be used to assess DIR spatial accuracy within a narrow uncertainty range. 2009 Institute of Physics and Engineering in Medicine.},
author = {Castillo, Richard and Castillo, Edward and Guerra, Rudy and Johnson, Valen E. and McPhail, Travis and Garg, Amit K. and Guerrero, Thomas},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Castillo et al. - 2009 - A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets.pdf:pdf},

journal = {Physics in Medicine and Biology},
number = {7},
pages = {1849--1870},
title = {{A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets}},
volume = {54},
year = {2009}
}
@techreport{Ranjan,
abstract = {We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions ; these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96{\%} smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small ({\textless} 1 pixel), a convolutional approach applied to pairs of warped images is appropriate. Third, unlike FlowNet, the learned convolution filters appear similar to classical spatio-temporal filters, giving insight into the method and how to improve it. Our results are more accurate than FlowNet on most standard benchmarks , suggesting a new direction of combining classical flow methods with deep learning.},
annote = {Pyramid-approach to motion estimation

Problem: Estimate two-frame optical flow using an end-to-end deep learning approach},
author = {Ranjan, Anurag and Black, Michael J},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ranjan, Black - Unknown - Optical Flow Estimation using a Spatial Pyramid Network.pdf:pdf},
title = {{Optical Flow Estimation using a Spatial Pyramid Network}},

}
@article{Dalca2018,
abstract = {Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu .},
annote = {Unsupervised, propabalistic

Voxelpmorph V2.0},
archivePrefix = {arXiv},
arxivId = {1805.04605},
author = {Dalca, Adrian V. and Balakrishnan, Guha and Guttag, John and Sabuncu, Mert R.},

eprint = {1805.04605},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dalca et al. - 2018 - Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration.pdf:pdf},
journal = {Medical Image Computing and Computer Assisted Intervention},
pages = {729--738},
title = {{Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration}},

year = {2018}
}
@article{jaderberg2015spatial,
abstract = {Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.},
author = {Jaderberg, Max and Simonyan, Karen and Zisserman, Andrew and Kavukcuoglu, Koray},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Jaderberg et al. - 2015 - Spatial Transformer Networks.pdf:pdf},
journal = {Advances in Neural Information Processing Systems},
keywords = {NN},
mendeley-tags = {NN},
title = {{Spatial Transformer Networks}},
year = {2015}
}
@article{grenander1998computational,
author = {Grenander, Ulf and Miller, Michael I.},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Grenander, Miller - 1998 - Computational Anatomy An Emerging Discipline.pdf:pdf},
journal = {Quarterly ofApplied Mathematics},
pages = {617-- 694},
title = {{Computational Anatomy: An Emerging Discipline}},

volume = {56},
year = {1998}
}
@article{Rueckert1999,
abstract = {In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. {\textcopyright} 1999 IEEE.},
annote = {B-splines paper},
author = {Rueckert, Daniel and Sonoda, Luke I and Hayes, Carmel and Hill, Derek LG and Leach, Martin O and Hawkes, David J},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Rueckert et al. - 1999 - Nonrigid registration using free-form deformations Application to breast mr images.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
number = {8},
pages = {712--721},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Nonrigid registration using free-form deformations: Application to breast mr images}},
volume = {18},
year = {1999}
}
@article{di2014autism,
author = {{Di Martino}, Adriana and Yan, Chao-Gan and Li, Qingyang and Others},
journal = {Molecular psychiatry},
number = {6},
pages = {659--667},
publisher = {Nature Publishing Group},
title = {{The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism}},
volume = {19},
year = {2014}
}
@article{Dalca2019,
abstract = {We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library at http://voxelmorph.csail.mit.edu.},
archivePrefix = {arXiv},
arxivId = {1908.02738},
author = {Dalca, Adrian V. and Rakic, Marianne and Guttag, John and Sabuncu, Mert R.},
eprint = {1908.02738},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dalca et al. - 2019 - Learning Conditional Deformable Templates with Convolutional Networks.pdf:pdf},
journal = {Advances in Neural Information Processing Systems},
pages = {806----818},
title = {{Learning Conditional Deformable Templates with Convolutional Networks}},

year = {2019}
}
@article{Avants2008,
abstract = {One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.},
annote = {SyN, ANTs paper
Great evaluation!},
author = {Avants, B. B. and Epstein, C. L. and Grossman, M. and Gee, J. C.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Avants et al. - 2008 - Symmetric diffeomorphic image registration with cross-correlation Evaluating automated labeling of elderly and ne.pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Cross-correlation,Deformable image registration,Dementia,Diffeomorphic,Human cortex,Morphometry},
number = {1},
pages = {26--41},
pmid = {17659998},
title = {{Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain}},
volume = {12},
year = {2008}
}
@article{lamontagne2019oasis,
author = {LaMontagne, Pamela J and Benzinger, Tammie L S and Morris, John C and Others},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
title = {{OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease}},
year = {2019}
}
@article{Yang2017,
annote = {fully supervised (by algorithmically obtained shooting LDDMM data)

The method retains all theoretical properties of LDDMM and results in diffeomorphic transformations if appropriately regularized, but computes these transformations an order ofmagnitude faster than a GPU-
based optimization for the LDDMM model. Further speedups can be achieved by using multile GPUs. In effect, our Quicksilver registration approach converts a notoriously slow and memory-hungry registration approach to a fast method, while retaining all of its appealing mathematical properties. General solution that can be applied to many registration algorithms (e.g., elastic registration or SVF), and even parameterized such as B-splines.

First significant registration paper using deep learning.

Utelizes the LDDMM shooting algorithm in two ways:
1) to generate a ground truth of initial momentum, which is then learned by NN
2) to generate the predicted transformation by integrating the shooting equation (Eq. 4) for the prediced initial momentum

This dual-approach between deep learning and classical registration algorithms allows for the preservation of the theoretical guarantee of obtaining a diffeomorphism, even if our NN predicts a non-smooth initial momentum.


Uses a two-step process to estimating the momentum: 
(1) an Initial prediction, and a 
(2) correction prediction. 
(1) is trained first. Only then, (2) is trained. Suprisingly, this provides a VERY significant improvement over just training (1) for longer (Figure 4). Predictions of both networks are summed together for the final prediction.

Evaluation:
- Testing multiple different versions of the proposed method. Eg: different parameterization maps learner, Dropout/No dropout, different strides, different data sizes. Paper framed in a whay which first resents the best performing method, then confirms this method by stating other options tested, wich perform worse.
- Showing examples (Figure 5) of moving image, target image, result using different methods. Slices in all three dimensions (x, y, z) shown.
- compare mean target overlap (mean over all anatomical regions) to a variety of learned and classical algorithms, on multiple datasets (Fig 6). Explicity plotting of outliers. In-depth discussion
- fine tuning of the methods presented
- Study significance of improvement: paired t-tests with respect to mean overlap scores. Formulate null-hypothesis.},
archivePrefix = {arXiv},
arxivId = {1703.10908},
author = {Yang, Xiao and Kwitt, Roland and Styner, Martin and Niethammer, Marc},

eprint = {1703.10908},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Yang et al. - 2017 - Quicksilver Fast predictive image registration - A deep learning approach.pdf:pdf},

journal = {NeuroImage},
keywords = {Brain imaging,Deep learning,Image registration},
pages = {378--396},
publisher = {Academic Press Inc.},
title = {{Quicksilver: Fast predictive image registration - A deep learning approach}},
volume = {158},
year = {2017}
}
@article{Sabuncu2009,
abstract = {A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates.},
author = {Sabuncu, Mert R and Yeo, B T Thomas and {Van Leemput}, Koen and Vercauteren, Tom and Golland, Polina},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Sabuncu et al. - 2009 - Asymmetric image-template registration.pdf:pdf},
journal = {Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
number = {Pt 1},
pages = {565--73},
pmid = {20426033},
title = {{Asymmetric image-template registration.}},

volume = {12},
year = {2009}
}
@article{Vishnevskiy2017,
abstract = {Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared ℓ2-norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces. We further develop the TV-regularization for parametric displacement fields and provide an efficient numerical solution scheme using the Alternating Directions Method of Multipliers (ADMM). The proposed method was successfully applied to four clinical databases which capture breathing motion, including CT lung and MR liver images. It provided accurate registration results for the whole volume. A key strength of our proposed method is that it does not depend on organ masks that are conventionally required by many algorithms to avoid errors at sliding interfaces. Furthermore, our method is robust to parameter selection, allowing the use of the same parameters for all tested databases. The average target registration error (TRE) of our method is superior (10{\%} to 40{\%}) to other techniques in the literature. It provides precise motion quantification and sliding detection with sub-pixel accuracy on the publicly available breathing motion databases (mean TREs of 0.95 mm for DIR 4D CT, 0.96 mm for DIR COPDgene, 0.91 mm for POPI databases).},
author = {Vishnevskiy, Valery and Gass, Tobias and Szekely, Gabor and Tanner, Christine and Goksel, Orcun},


journal = {IEEE Transactions on Medical Imaging},
keywords = {4DCT,4DMR,ADMM,breathing motion,sliding at anatomical interfaces},
number = {2},
pages = {385--395},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Isotropic Total Variation Regularization of Displacements in Parametric Image Registration}},
volume = {36},
year = {2017}
}
@incollection{Liu2019,
abstract = {Brain image registration transforms a pair of images into one system with the matched imaging contents, which is of essential importance for brain image analysis. This paper presents a novel framework for unsupervised 3D brain image registration by capturing the feature-level transformation relationships between the unaligned image and reference image. To achieve this, we develop a feature-level probabilistic model to provide the direct regularization to the hidden layers of two deep convolutional neural networks, which are constructed from two input images. This model design is developed into multiple layers of these two networks to capture the transformation relationships at different levels. We employ two common benchmark datasets for 3D brain image registration and perform various experiments to evaluate our method. Experimental results show that our method clearly outperforms state-of-the-art methods on both benchmark datasets by a large margin.},
annote = {unsupervised

NN-based brain registration. Using a pyramid of probabilistic transformation maps (mean, std for each of them), then averaging them together.

Similar to Paper Dual-Stream Pyramid Registration network. Proposes a network architecture for same task.},
archivePrefix = {arXiv},
arxivId = {1907.01922},
author = {Liu, Lihao and Hu, Xiaowei and Zhu, Lei and Heng, Pheng-Ann},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1907.01922},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu et al. - 2019 - Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration.pdf:pdf},


pages = {346--354},
title = {{Probabilistic Multilayer Regularization Network for Unsupervised 3D Brain Image Registration}},
year = {2019}
}
@techreport{thirion1998,
abstract = {In this paper, we present the concept of diffusing models to perform image-to-image matching. Having two images to match, the main idea is to consider the objects boundaries in one image as semi-permeable membranes and to let the other image, considered as a deformable grid model, diffuse through these interfaces, by the action of effectors situated within the membranes. We illustrate this concept by an analogy with Maxwell's demons. We show that this concept relates to more traditional ones, based on attraction, with an intermediate step being optical flow techniques. We use the concept of diffusing models to derive three different non-rigid matching algorithms, one using all the intensity levels in the static image, one using only contour points, and a last one operating on already segmented images. Finally, we present results with synthesized deformations and real medical images, with applications to heart motion tracking and three-dimensional inter-patients matching.},
author = {Thirion, Jean-Philippe},
booktitle = {Medical Image Analysis},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Thirion - 1998 - Image matching as a diffusion process an analogy with Maxwell's demons.pdf:pdf},
keywords = {deformable model,elastic matching,image sequence analysis,inter-patient registration,non-rigid matching},
number = {3},
pages = {243--260},
publisher = {Elsevier},
title = {{Image matching as a diffusion process: an analogy with Maxwell's demons}},

volume = {2},
year = {1998}
}
@article{Castillo2013,
abstract = {Landmark point-pairs provide a strategy to assess deformable image registration (DIR) accuracy in terms of the spatial registration of the underlying anatomy depicted in medical images. In this study, we propose to augment a publicly available database (www.dir-lab.com) of medical images with large sets of manually identified anatomic feature pairs between breath-hold computed tomography (BH-CT) images for DIR spatial accuracy evaluation. Ten BH-CT image pairs were randomly selected from the COPDgene study cases. Each patient had received CT imaging of the entire thorax in the supine position at one-fourth dose normal expiration and maximum effort full dose inspiration. Using dedicated in-house software, an imaging expert manually identified large sets of anatomic feature pairs between images. Estimates of inter- and intra-observer spatial variation in feature localization were determined by repeat measurements of multiple observers over subsets of randomly selected features. 7298 anatomic landmark features were manually paired between the 10 sets of images. Quantity of feature pairs per case ranged from 447 to 1172. Average 3D Euclidean landmark displacements varied substantially among cases, ranging from 12.29 (SD: 6.39) to 30.90 (SD: 14.05) mm. Repeat registration of uniformly sampled subsets of 150 landmarks for each case yielded estimates of observer localization error, which ranged in average from 0.58 (SD: 0.87) to 1.06 (SD: 2.38) mm for each case. The additions to the online web database (www.dir-lab.com) described in this work will broaden the applicability of the reference data, providing a freely available common dataset for targeted critical evaluation of DIR spatial accuracy performance in multiple clinical settings. Estimates of observer variance in feature localization suggest consistent spatial accuracy for all observers across both four-dimensional CT and COPDgene patient cohorts. {\textcopyright} 2013 Institute of Physics and Engineering in Medicine.},
author = {Castillo, Richard and Castillo, Edward and Fuentes, David and Ahmad, Moiz and Wood, Abbie M. and Ludwig, Michelle S. and Guerrero, Thomas},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Castillo et al. - 2013 - A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study arch.pdf:pdf},

journal = {Physics in Medicine and Biology},
number = {9},
pages = {2861--2877},
title = {{A reference dataset for deformable image registration spatial accuracy evaluation using the COPDgene study archive}},
volume = {58},
year = {2013}
}
@book{schmidt2014registration,
author = {Schmidt-Richberg, Alexander},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Schmidt-Richberg - 2014 - Registration methods for pulmonary image analysis integration of morphological and physiological knowledge.pdf:pdf},

keywords = {fissure alignment,lung,registration,segmentation,sliding motion},
publisher = {Springer Science $\backslash${\&} Business Media},
title = {{Registration methods for pulmonary image analysis: integration of morphological and physiological knowledge}},
year = {2014}
}
@article{Li2012,
abstract = {This paper presents registration via embedded maps (REM), a deformable registration algorithm for images with varying topology. The algorithm represents 3-D images as 4-D manifolds in a Riemannian space (referred to as embedded maps). Registration is performed as a surface evolution matching one embedded map to another using a diffusion process. The approach differs from those existing in that it takes an apriori estimation of image regions where topological changes are present, for example lesions, and generates a dense vector field representing both the shape and intensity changes necessary to match the images. The algorithm outputs both a diffeomorphic deformation field and an intensity displacement which corrects the intensity difference caused by topological changes. Multiple sets of experiments are conducted on magnetic resonance imaging (MRI) with lesions from OASIS and ADNI datasets. These images are registered to either a brain template or images of healthy individuals. An exemplar case registering a template to an MRI with tumor is also given. The resulting deformation fields were compared with those obtained using diffeomorphic demons, where topological changes are not modeled. These sets of experiments demonstrate the efficacy of our proposed REM method for registration of brain MRI with severe topological differences. {\textcopyright} 2012, IEEE.},
author = {Li, Xiaoxing and Long, Xiaojing and Wyatt, Christopher and Laurienti, Paul},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Li et al. - 2012 - Registration of Images with Varying Topology Using Embedded Maps.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {Deformable registration,Riemannian embedding,false deformation,topological change},
number = {3},
pages = {749--765},
pmid = {22194239},
title = {{Registration of Images with Varying Topology Using Embedded Maps}},
volume = {31},
year = {2012}
}
@misc{fischl2012freesurfer,
author = {Fischl, B},
title = {{FreeSurfer. Neurolmage, 62 (2), 774--781}},
year = {2012}
}
@inproceedings{Isola2017,
abstract = {Labels to Facade BW to Color Aerial to Map Labels to Street Scene Edges to Photo input output input input input input output output output output input output Day to Night Figure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output image. These problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to pixels. Conditional adversarial nets are a general-purpose solution that appears to work well on a wide variety of these problems. Here we show results of the method on several. In each case we use the same architecture and objective, and simply train on different data. Abstract We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.},
author = {Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A and Research, Berkeley Ai},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Isola et al. - 2017 - Image-to-Image Translation with Conditional Adversarial Networks.pdf:pdf},
pages = {1125--1134},
title = {{Image-to-Image Translation with Conditional Adversarial Networks}},

year = {2017}
}
@techreport{Hu,
author = {Hu, Xiaojun and Kang, Miao and Huang, Weilin and Scott, Matthew R and Wiest, Roland and Reyes, Mauricio},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - Unknown - Dual-Stream Pyramid Registration Network Supplementary Material.pdf:pdf},
title = {{Dual-Stream Pyramid Registration Network Supplementary Material}}
}
@article{Darkner2018,
abstract = {Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is invertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms. We present CDD (Collocation for Diffeomorphic Deformations), a numerical solution to diffeomorphic image registration, which solves for the Stationary Velocity Field (SVF) using an implicit A-stable collocation method. CDD guarantees the preservation of the diffeomorphic properties at all discrete points and is thereby consistent to machine precision. We compared CDD's collocation method with the following standard methods: Scaling and Squaring, Forward Euler, and Runge-Kutta 4, and found that CDD is up to 9 orders of magnitude more consistent. Finally, we evaluated CDD on a number of standard bench-mark data sets and compared the results with current state-of-the-art methods: SPM-DARTEL, Diffeomorphic Demons and SyN. We found that CDD outperforms state-of-the-art methods in consistency and delivers comparable or superior registration precision.},
annote = {proposes a consistent registration model CCD.

Consitent means transformation and its inverse are consistent, thus ||x - W(W-1(x))|| is approx 0. Consistency is required for a truly bijective mapping.

Uses transformation based on a Stationary Velocity Field (SVF)},
author = {Darkner, Sune and Pai, Akshay and Liptrot, Matthew G. and Sporring, Jon},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Darkner et al. - 2018 - Collocation for Diffeomorphic Deformations in Medical Image Registration.pdf:pdf},

journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Registration,convergence and stability,image processing and computer vision,model validation and analysis,ordinary differential equation},
number = {7},
pages = {1570--1583},
publisher = {IEEE Computer Society},
title = {{Collocation for Diffeomorphic Deformations in Medical Image Registration}},
volume = {40},
year = {2018}
}
@article{Nielsen2019,
author = {Nielsen, Rune Kok and Feragen, Aasa and Darkner, Sune},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nielsen, Feragen, Darkner - 2019 - Modeling Discontinuities in Diffeomorphic Registration.pdf:pdf},
keywords = {rune thesis,rune2019thesis},
mendeley-tags = {rune thesis},
title = {{Modeling Discontinuities in Diffeomorphic Registration}},
year = {2019}
}
@article{balakrishnan2019voxel,
abstract = {We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this work, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to state-of-the-art methods, while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time, and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is freely available at voxelmorph.csail.mit.edu.},
annote = {supervised + unsupervised approach

Initial vexelmorph paper, later followed up by "Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration"
lots of good reasoning about using ML for registration

good overview over the field

Evaluation: 
Dataset:
- all displayed in 2D, but registered in 3d
3731 T1–weighted brain MRI scans from multiple datasets:
OASIS, ABIDE, ADHD200, MCIC, PPMI, HABS, Harvard GSP, FreeSurfer Buckner40
- 30 class anatomical segmentation used for evaluation
- metrics: dice overlap, smoothness of transformation (via Jacobian), diffeomorphism of transformation (jacobian determinant)
- Comparison to baseline models: Symmetric Normalization (SyN) from ANTs software package


Section V-B:
method: registering to an atlas (one averaged, common model).
VoxelMorph is compared to ANTs SyN, NiftyReg and Affine registration. Compared metrics: Dice overlap of segmentation data, runtime (GPU and CPU)
- resuts: table and plot of metrics, aggregated and per anatomical feature

Section V-C:
hyper parameter tuning plots

Section V-D:
effect of training set size

Section V-E:
crosscheck of results on a dataset of manual segmentations. Nessesary as all other segmentations were automatically obtained by CNN


Section V-F:
evaluation on unseen test set

Secton V-G:
Same model, but now with extra segmentation data used during training},
author = {Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R. and Guttag, John and Dalca, Adrian V.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Balakrishnan et al. - 2019 - VoxelMorph A Learning Framework for Deformable Medical Image Registration.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {NN},
mendeley-tags = {NN},
number = {8},
pages = {1788--1800},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
title = {{VoxelMorph: A Learning Framework for Deformable Medical Image Registration}},
volume = {38},
year = {2019}
}
@article{Hoffmann2020,
abstract = {We introduce a learning strategy for contrast-invariant image registration without requiring imaging data. While classical registration methods accurately estimate the spatial correspondence between images, they solve a costly optimization problem for every image pair. Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model for diverse label maps and images that exposes networks to a wide range of variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary image contrasts without the need to retrain for new modalities. We demonstrate registration accuracy that most often surpasses the state of the art both within and across modalities, using a single model. Critically, we show that input labels from which we synthesize images need not be of actual anatomy: training on randomly generated geometric shapes also results in competitive registration performance, albeit slightly less accurate, while alleviating the dependency on real data of any kind. Our code is available at: http://voxelmorph.csail.mit.edu},
annote = {- randomly generated segmentation maps
- use generative model to generate images of different modalities/contrasts from the label maps
- train image registration in a supervised manner, also across modalities
- registration model generalizes to real data},
archivePrefix = {arXiv},
arxivId = {2004.10282},
author = {Hoffmann, Malte and Billot, Benjamin and Iglesias, Juan Eugenio and Fischl, Bruce and Dalca, Adrian V.},
eprint = {2004.10282},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hoffmann et al. - 2020 - Learning Multi-Modal Image Registration without Real Data.pdf:pdf},
title = {{Learning image registration without images}},

year = {2020}
}
@incollection{CollFont2019,
annote = {Kidney registration based on alignment of K-means clusters. The presented method makes 4D medical images more consistent, leading to an improved diagnosis in downstream tasks.

Very medical-application focused},
author = {Coll-Font, Jaume and Afacan, Onur and Chow, Jeanne and Kurugol, Sila},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Coll-Font et al. - 2019 - Linear Time Invariant Model Based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI.pdf:pdf},


pages = {430--437},
title = {{Linear Time Invariant Model Based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI}},
year = {2019}
}
@article{Lee2013,
abstract = {We present a novel image registration method based on B-spline free-form deformation that simultaneously optimizes particle correspondence and image similarity metrics. Different from previous B-spline based registration methods optimized w.r.t. the control points, the deformation in our method is estimated from a set of dense unstructured pair of points, which we refer as corresponding particles. As intensity values are matched on the corresponding location, the registration performance is iteratively improved. Moreover, the use of corresponding particles naturally extends our method to a group-wise registration by computing a mean of particles. Motivated by a surface-based group-wise particle correspondence method, we developed a novel system that takes such particles to the image domain, while keeping the spirit of the method similar. The core algorithm both minimizes an entropy based group-wise correspondence metric as well as maximizes the space sampling of the particles. We demonstrate the results of our method in an application of rodent brain structure segmentation and show that our method provides better accuracy in two structures compared to other registration methods. {\textcopyright} 2013 Springer-Verlag.},
annote = {aligns particales, with goal of:
1) positional overlap of particles across images (1-1 matching)
2) intensity match},
author = {Lee, Joohwi and Lyu, Ilwoo and Oǧuz, Ipek and Styner, Martin A.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lee et al. - 2013 - Particle-guided image registration.pdf:pdf},


journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages = {203--210},
pmid = {24505762},
publisher = {Springer, Berlin, Heidelberg},
title = {{Particle-guided image registration}},

year = {2013}
}
@article{szeliski2006tutorial,
annote = {cite book piblication in "Handbook of Mathematical Models in Computer Vision" instead},
author = {Szeliski, Richard},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Szeliski - 2006 - Image Alignment and Stitching A Tutorial.pdf:pdf},

journal = {Foundations and Trends in Computer Graphics and Vision},
keywords = {affine,device coordinates,motion models,normalized coordinates,overview,taylor series,tutorial},
mendeley-tags = {affine,device coordinates,motion models,normalized coordinates,overview,taylor series,tutorial},
number = {1},
pages = {1--104},
title = {{Image Alignment and Stitching: A Tutorial}},
volume = {2},
year = {2006}
}
@incollection{Alven2019,
annote = {NN, only affine and rigid transfomations. Unsupervised.

Affine alignment of images via NN.

Interesting idea: instead of predicting the whole transformation in on step, we use a composition of multiple smaller transformations. Each small transformation is preduced by the same NN, with shared weigths.

Application: spartial normalization of positron emission tomography (PET) images via a NN.

A PET scan does not provide sufficient anatomical information. Registration is required to align the PET scan to a coordinate system. This is often done with supplemental MR images. We learn a NN instead, which does not require MR images.},
author = {Alv{\'{e}}n, Jennifer and Heurling, Kerstin and Smith, Ruben and Strandberg, Olof and Sch{\"{o}}ll, Michael and Hansson, Oskar and Kahl, Fredrik},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Alv{\'{e}}n et al. - 2019 - A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images.pdf:pdf},


pages = {355--363},
title = {{A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images}},
year = {2019}
}
@techreport{Fechter2020,
abstract = {Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to a 3D dataset the algorithm calculates the inverse of the registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy. Possible applications are the use as a stand-alone algorithm for 3D and 4D motion tracking or in the beginning of studies until enough datasets for a separate training phase are available.},
annote = {Summary:
patch-based, unsupervised, one-shot learning (training one one input image) with good results. avieves an average landmark distance of 1.77mm +- 2.07 on extreme phases of dirlab dataset. Uses multiple scales, compares to many other works on the dirlab dataset.


Data:
31 patients, many images per patient
Prediction: given a time series of N images, we align each image n to the following n+1
Additionally, extreme phase registration is evaluated

This leads to small deformations. Contrast to my approach: register the two extreme images --{\textgreater} much larger deformations

Data pre-processing:
patch based learning, only consider patches of the body (no background)

Patches:
performs patch-based learning with non-overlapping. Predicts a SVF for each patch, then appends them to the whole image. Patches taken at different scales, to enable large deformations

Model:
shallow unet (2 layers). 3 Convolutions per layer
3 separet strands (different weights, trained separately), for different resolutions

Training:
Performs one-shot learning. The model is fine-tuned for each pair of images before prediction.

Evaluation:
Landmarks: landmark distance
Segmentation: S{\o}rensen-Dice index, Hausdorff distance, average symmetric surface distance
Diffeomorphism: determinant of the Jacobian matrix

Results:
achieves landmark distance of 1.77mm +- 2.07 on extreme phases of dirlab datasets (our VXM: 3.2mm on test data)},
archivePrefix = {arXiv},
arxivId = {1907.04641v3},
author = {Fechter, Tobias and Baltas, Dimos},
eprint = {1907.04641v3},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Fechter, Baltas - 2020 - One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking.pdf:pdf},
keywords = {Index Terms-Machine learning,Motion compensation and analysis,Neural network,Registration},
title = {{One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking}},

year = {2020}
}
@article{Delmon2013,
abstract = {Sliding motion is a challenge for deformable image registration because it leads to discontinuities in the sought deformation. In this paper, we present a method to handle sliding motion using multiple B-spline transforms. The proposed method decomposes the sought deformation into sliding regions to allow discontinuities at their interfaces, but prevents unrealistic solutions by forcing those interfaces to match. The method was evaluated on 16 lung cancer patients against a single B-spline transform approach and a multi B-spline transforms approach without the sliding constraint at the interface. The target registration error (TRE) was significantly lower with the proposed method (TRE = 1.5 mm) than with the single B-spline approach (TRE = 3.7 mm) and was comparable to the multi B-spline approach without the sliding constraint (TRE = 1.4 mm). The proposed method was also more accurate along region interfaces, with 37{\%} less gaps and overlaps when compared to the multi B-spline transforms without the sliding constraint.},
author = {Delmon, V and Rit, S and Pinho, R and Sarrut, D},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Delmon et al. - 2013 - Registration of sliding objects using direction dependent B-splines decomposition Registration of sliding objects.pdf:pdf},
journal = {Phys. Med. Bio},
number = {5},
pages = {1303--1314},
title = {{Registration of sliding objects using direction dependent B-splines decomposition Registration of sliding objects using direction dependent B-splines decomposition *}},

volume = {58},
year = {2013}
}
@article{Avants2011,
abstract = {The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard = 0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard = 0.669. ±. 0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. {\textcopyright} 2010 Elsevier Inc.},
author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook, Philip A. and Klein, Arno and Gee, James C.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Avants et al. - 2011 - A reproducible evaluation of ANTs similarity metric performance in brain image registration.pdf:pdf},

journal = {NeuroImage},
number = {3},
pages = {2033--2044},
pmid = {20851191},
publisher = {Academic Press},
title = {{A reproducible evaluation of ANTs similarity metric performance in brain image registration}},
volume = {54},
year = {2011}
}
@incollection{Bhalodia2019,
abstract = {Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce anatomically feasible correspondences. This is usually enforced through some smoothness-based generic regularization on deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware (i.e., data independent) fail. Recently, deep networks have been for unsupervised image registration, these methods are computationally faster and maintains the accuracy of state of the art methods. However, these networks use smoothness penalty on deformation fields and ignores population-level statistics of the transformations. We propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration. This regularization is in the form of a bottleneck autoencoder, which encodes the population level information of the deformation fields in a low-dimensional manifold. The proposed architecture produces deformation fields that describe the population-level features and associated correspondences in an anatomically relevant manner and are statistically compact relative to the state-of-the-art approaches while maintaining computational efficiency. We demonstrate the efficacy of the proposed architecture on synthetic data sets, as well as 2D and 3D medical data.},
annote = {Unsupervised

Regularization performed by an Auto-Encoder

idea: a Auto-encoder with a low dimensionality latent space can only express simple, sensible transformations. It can further adapt to transformations present in the population it is trained on.},
archivePrefix = {arXiv},
arxivId = {1908.05825},
author = {Bhalodia, Riddhish and Elhabian, Shireen Y. and Kavan, Ladislav and Whitaker, Ross T.},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1908.05825},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Bhalodia et al. - 2019 - A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration.pdf:pdf},


pages = {391--400},
title = {{A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration}},
year = {2019}
}
@incollection{Lee2019a,
abstract = {Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or landmarks. We introduce a novel, generic framework, Image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task. Thanks to these representations we can employ a test-specific, iterative refinement over the transformation parameters which yields highly accurate registration even with very limited training data. Performance is demonstrated on pairwise 3D brain registration and illustrative synthetic data.},
annote = {Supervised

Use an intermediate 'Image Transformer Module' to augment images and hightlight structures of interest for the registration.

Make a good point for the power of feature based similarity metrics with synthetic example.

Requires annotations of the structures of interest.},
archivePrefix = {arXiv},
arxivId = {1907.09200},
author = {Lee, Matthew C. H. and Oktay, Ozan and Schuh, Andreas and Schaap, Michiel and Glocker, Ben},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1907.09200},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lee et al. - 2019 - Image-and-Spatial Transformer Networks for Structure-Guided Image Registration(2).pdf:pdf},


pages = {337--345},
title = {{Image-and-Spatial Transformer Networks for Structure-Guided Image Registration}},
year = {2019}
}
@article{Vercauteren2008,
abstract = {Modern morphometric studies use non-linear image registration to compare anatomies and perform group analysis. Recently, log-Euclidean approaches have contributed to promote the use of such computational anatomy tools by permitting simple computations of statistics on a rather large class of invertible spatial transformations. In this work, we propose a non-linear registration algorithm perfectly fit for log-Euclidean statistics on diffeomorphisms. Our algorithm works completely in the log-domain, i.e. it uses a stationary velocity field. This implies that we guarantee the invertibility of the deformation and have access to the true inverse transformation. This also means that our output can be directly used for log-Euclidean statistics without relying on the heavy computation of the log of the spatial transformation. As it is often desirable , our algorithm is symmetric with respect to the order of the input images. Furthermore, we use an alternate optimization approach related to Thirion's demons algorithm to provide a fast non-linear registration algorithm. First results show that our algorithm outperforms both the demons algorithm and the recently proposed diffeomorphic demons algorithm in terms of accuracy of the transformation while remaining com-putationally efficient.},
author = {Vercauteren, Tom and Pennec, Xavier and Perchant, Aymeric and Ayache, Nicholas},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Vercauteren et al. - 2008 - Symmetric Log-Domain Diffeomorphic Registration A Demons-based Approach.pdf:pdf},
journal = {Medical Image Computing and Computer Assisted Intervention},
pages = {754--761},
title = {{Symmetric Log-Domain Diffeomorphic Registration: A Demons-based Approach}},

year = {2008}
}
@incollection{szeliski2006,
address = {Boston, MA},
author = {Szeliski, Richard},
booktitle = {Handbook of Mathematical Models in Computer Vision},
editor = {Paragios, Nikos and Chen, Yunmei and Faugeras, Olivier},
pages = {273--292},
publisher = {Springer US},
title = {{Image Alignment and Stitching}},
year = {2006}
}
@article{Lavoie2012,
author = {Lavoie, Caroline and Higgins, Jane and Bissonnette, Jean Pierre and Le, Lisa W. and Sun, Alexander and Brade, Anthony and Hope, Andrew and Cho, John and Bezjak, Andrea},


journal = {International Journal of Radiation Oncology Biology Physics},
number = {5},
pages = {1086--1092},
title = {{Volumetric image guidance using carina vs spine as registration landmarks for conventionally fractionated lung radiotherapy}},

volume = {84},
year = {2012}
}
@techreport{Zanfir2018,
abstract = {The problem of graph matching under node and pairwise constraints is fundamental in areas as diverse as combinatorial optimization, machine learning or computer vision, where representing both the relations between nodes and their neighborhood structure is essential. We present an end-to-end model that makes it possible to learn all parameters of the graph matching process, including the unary and pairwise node neighborhoods, represented as deep feature extraction hierarchies. The challenge is in the formulation of the different matrix computation layers of the model in a way that enables the consistent, efficient propagation of gradients in the complete pipeline from the loss function, through the combinatorial optimization layer solving the matching problem, and the feature extraction hierarchy. Our computer vision experiments and ablation studies on challenging datasets like PASCAL VOC keypoints, Sintel and CUB show that matching models refined end-to-end are superior to counterparts based on feature hierarchies trained for other problems.},
annote = {presentation: https://youtu.be/NHajBzgnC1Q?t=2166},
author = {Zanfir, Andrei and Sminchisescu, Cristian},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Zanfir, Sminchisescu - 2018 - Deep Learning of Graph Matching.pdf:pdf},


pages = {2684--2693},
title = {{Deep Learning of Graph Matching}},
year = {2018}
}
@article{Czolbe2020,
abstract = {To train Variational Autoencoders (VAEs) to generate realistic imagery requires a loss function that reflects human perception of image similarity. We propose such a loss function based on Watson's perceptual model, which computes a weighted distance in frequency space and accounts for luminance and contrast masking. We extend the model to color images, increase its robustness to translation by using the Fourier Transform, remove artifacts due to splitting the image into blocks, and make it differentiable. In experiments, VAEs trained with the new loss function generated realistic, high-quality image samples. Compared to using the Euclidean distance and the Structural Similarity Index, the images were less blurry; compared to deep neural network based losses, the new approach required less computational resources and generated images with less artifacts.},
archivePrefix = {arXiv},
arxivId = {2006.15057},
author = {Czolbe, Steffen and Krause, Oswin and Cox, Ingemar and Igel, Christian},
eprint = {2006.15057},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Czolbe et al. - 2020 - A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model.pdf:pdf},
journal = {Advances in Neural Information Processing Systems},
title = {{A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model}},

year = {2020}
}
@article{Haskins2019,
abstract = {The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.},
annote = {Review paper, overview over lots of papers},
archivePrefix = {arXiv},
arxivId = {1903.02026},
author = {Haskins, Grant and Kruger, Uwe and Yan, Pingkun},
eprint = {1903.02026},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Haskins, Kruger, Yan - 2019 - Deep Learning in Medical Image Registration A Survey.pdf:pdf},
title = {{Deep Learning in Medical Image Registration: A Survey}},

year = {2019}
}
@incollection{Hu2019,
abstract = {We propose a Dual-Stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which explores a single-stream encoder-decoder network to compute a . Our contributiregistration fields from a pair of 3D volumes, we design a two-stream architecture able to compute multi-scale registration fields from convolutional feature pyramidsons are two-fold: (i) we design a two-stream 3D encoder-decoder network which computes two convolutional feature pyramids separately for a pair of input volumes, resulting in strong deep representations that are meaningful for deformation estimation; (ii) we propose a pyramid registration module able to predict multi-scale registration fields directly from the decoding feature pyramids. This allows it to refine the registration fields gradually in a coarse-to-fine manner via sequential warping, and enable the model with the capability for handling significant deformations between two volumes, such as large displacements in spatial domain or slice space. The proposed Dual-PRNet is evaluated on two standard benchmarks for brain MRI registration, where it outperforms the state-of-the-art approaches by a large margin, e.g., having improvements over recent VoxelMorph [2] with 0.683-{\textgreater}0.778 on the LPBA40, and 0.511-{\textgreater}0.631 on the Mindboggle101, in term of average Dice score.},
annote = {Unsupervised

Using a multi-stage approach to learn a registration map. Two parallel U-Nets interacting with each other

Simple architecture, similar to multi-stage segmentation (coarse to fine), and great results!

supplementary material available separately},
archivePrefix = {arXiv},
arxivId = {1909.11966},
author = {Hu, Xiaojun and Kang, Miao and Huang, Weilin and Scott, Matthew R. and Wiest, Roland and Reyes, Mauricio},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1909.11966},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2019 - Dual-Stream Pyramid Registration Network.pdf:pdf},


pages = {382--390},
title = {{Dual-Stream Pyramid Registration Network}},
year = {2019}
}
@article{DeVos2019,
annote = {Traditional deep learning

{\~{}}2000 CT-scans (non-public NLST (The National Lung Screening Trial Research Team) dataset)
+ each possible pair of intra-patient data is used for training
+ data augmentation

Normal NN training

Fig 16: landmark errors

Resuklts:
Dirlab landmar error 2.64 ± 4.32},
archivePrefix = {arXiv},
arxivId = {1809.06130},
author = {de Vos, Bob D. and Berendsen, Floris F. and Viergever, Max A. and Sokooti, Hessam and Staring, Marius and I{\v{s}}gum, Ivana},

eprint = {1809.06130},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/de Vos et al. - 2019 - A deep learning framework for unsupervised affine and deformable image registration.pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Affine image registration,Cardiac cine MRI,Chest CT,Deep learning,Deformable image registration,Unsupervised learning},
pages = {128--143},
publisher = {Elsevier B.V.},
title = {{A deep learning framework for unsupervised affine and deformable image registration}},
volume = {52},
year = {2019}
}
@inproceedings{Ronneberger2015,
abstract = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.},
archivePrefix = {arXiv},
arxivId = {1505.04597},
author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

eprint = {1505.04597},


pages = {234--241},
publisher = {Springer Verlag},
title = {{U-net: Convolutional networks for biomedical image segmentation}},
volume = {9351},
year = {2015}
}
@techreport{thorin95,
author = {Thirion, Jean-Philippe},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Thirion - 1995 - Fast Non-Rigid Matching of 3D Medical Images.pdf:pdf},
title = {{Fast Non-Rigid Matching of 3D Medical Images}},

year = {1995}
}
@article{Christiansen2006,
annote = {example of how to teach a lesson:

1. Awake interest
2. simple Sub-assignment, so students familiarize with the material used in this lesson
3. Explore knowledge as the answer to a question: through concrete, applied problems, not the problem-removed representation from textbooks
4. Students form hypothesis based on assignments
5. Teach helps to validatehypothesis
6. teach generalizes knowledge gained},
author = {Christiansen, Frederik Voetmann and Olsen, Lars},
file = {:home/steffen/Downloads/christiansen-olsen-2006.pdf:pdf},
journal = {Mona},
pages = {7--23},
title = {{Analyse og design af didaktiske situationer [Analysis and design of didactic situations: a pharmaceutical example]}},
volume = {3},
year = {2006}
}
@article{Papiez2014,
abstract = {Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries.},
author = {Papiez, Bart{\l}omiej W. and Heinrich, Mattias P. and Fehrenbach, J{\'{e}}rome and Risser, Laurent and Schnabel, Julia A.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Papiez et al. - 2014 - An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration.pdf:pdf},

journal = {Medical Image Analysis},
keywords = {Bilateral filtering,Nonrigid registration,Regularisation,Respiratory motion,Sliding motion},
number = {8},
pages = {1299--1311},
publisher = {Elsevier},
title = {{An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration}},
volume = {18},
year = {2014}
}
@inproceedings{Li2018,
abstract = {A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different spatial resolutions with deep self-supervision through typical feedforward and backpropagation computation. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.},
annote = {more of a schale-space work....},
author = {Li, Hongming and Fan, Yong},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},



keywords = {Fully convolutional networks,Image registration,Multi-resolution,Self-supervision},
pages = {1075--1078},
publisher = {IEEE Computer Society},
title = {{Non-rigid image registration using self-supervised fully convolutional networks without training data}},
volume = {2018-April},
year = {2018}
}
@article{Wolper2020,
annote = {damage of fibrous structures

examples:
strong, stretchy fibers in a steak influence the direction the meat cuts and tears, while a succulent cube of pork belly has decadent layers that peel apart in sheets.},
author = {Wolper, Joshuah and Chen, Yunuo and Li, Minchen and Fang, Yu and Qu, Ziyin and Lu, Jiecong and Cheng, Meggie and Jiang, Chenfanfu},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Wolper et al. - 2020 - AnisoMPM Animating Anisotropic Damage Mechanics.pdf:pdf},

journal = {ACM Transactions on Graphics},
number = {4},
publisher = {Association for Computing Machinery (ACM)},
title = {{AnisoMPM: Animating Anisotropic Damage Mechanics}},
volume = {39},
year = {2020}
}
@incollection{Luo2019,
annote = {Introduces distinction between transformation uncertainty (the transformation field is unclear), and label uncertainty (labels from atlas, under an uncertain transformation). In larger regions of the brain, it it possible to have high transformation uncertainty, but low label uncertainty (Fig 4, Vc). On edges of high contrast (eg: vertices, tumors), it is also possible to have low transformation uncertainty, but high label uncertainty (Fig 4, Ve).

aplication example of uncertainty measurment in brain registration. Pre-marked tumor gets registered to patient during surgery.

Detailed discussion of uncertainty in registration, and how it can be mis-interpreted},
author = {Luo, Jie and Sedghi, Alireza and Popuri, Karteek and Cobzas, Dana and Zhang, Miaomiao and Preiswerk, Frank and Toews, Matthew and Golby, Alexandra and Sugiyama, Masashi and Wells, William M. and Frisken, Sarah},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Luo et al. - 2019 - On the Applicability of Registration Uncertainty.pdf:pdf},


pages = {410--419},
title = {{On the Applicability of Registration Uncertainty}},
year = {2019}
}
@misc{Blake2014,
author = {Blake, Canan and Scanlon, Eileen},

file = {:home/steffen/Downloads/online{\_}teaching.png:png},
pages = {186--200},
title = {{Online Teaching}},
year = {2014}
}
@article{Ulman2014,
author = {Ma{\v{s}}ka, Martin and Ulman, Vladim{\'{i}}r and Svoboda, David and Matula, Pavel and Matula, Petr and Ederra, Cristina and Urbiola, Ainhoa and Espa{\~{n}}a, Tom{\'{a}}s and Venkatesan, Subramanian and Balak, Deepak M W and Karas, Pavel and Bolckov{\'{a}}, Tereza and {\v{S}}treitov{\'{a}}, Mark{\'{e}}ta and Carthel, Craig and Coraluppi, Stefano and Harder, Nathalie and Rohr, Karl and Magnusson, Klas E G and Jald{\'{e}}n, Joakim and Blau, Helen M and Dzyubachyk, Oleh and Kr{\'{i}}{\v{z}}ek, Pavel and Hagen, Guy M and Pastor-Escuredo, David and Jimenez-Carretero, Daniel and Ledesma-Carbayo, Maria J and Mu{\~{n}}oz-Barrutia, Arrate and Meijering, Erik and Kozubek, Michal and Ortiz-de-Solorzano, Carlos},


journal = {Bioinformatics},
number = {11},
pages = {1609--1617},
title = {{A benchmark for comparison of cell tracking algorithms}},

volume = {30},
year = {2014}
}
@article{dalca2018unsuperiveddiff,
abstract = {Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu .},
archivePrefix = {arXiv},
arxivId = {1805.04605},
author = {Dalca, Adrian V. and Balakrishnan, Guha and Guttag, John and Sabuncu, Mert R.},

eprint = {1805.04605},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Dalca et al. - 2018 - Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration.pdf:pdf},
journal = {Medical Image Computing and Computer Assisted Intervention},
pages = {729--738},
title = {{Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration}},

year = {2018}
}
@article{rune2019TopAwaRe,
annote = {Proposes topologi-aware non-diffeomorphic registration

Evaluation:

MICCAI2012 database: 32 T1-weighted MRI 3D brain volumes. Annotated for anatomical regions.

all samples affine pre-registered to a common space

6 samples with expanded vertices identified

One group of all pairwhise registrations (32*31 = 992 pairs)

Other group of pairwhise registration between normal vertices and expanded vertices (26 * 6 = 156 pairs)

Manual annotation of ventricle discontinuety of one subject with contracted ventricles. Registered to all other subjects with closed ventrices. Used for later expansion via piecewhise-diffeomorphic approach.

Validation of diffeomorphic base-implementation: 
- method: global Dice overlap (a loss function for segmentation) of coregistered regions (segmentation annotation) of subject-to-subject registration
- result: our diffeomorphic code is in line with state of the art

Validation of piecewhise-diffeomorphic model:
- method 1: manual inspection of the (1) diffeomorphic and (2) piecewhise-diffeomorphic registration of 2 samples from contrated to expanded vertricles. Observation of the segmentation annotation for 5 classes locally present
- result 1: (1) streches out other classes than ventrices to fit. (2) expands ventricles from the discontenuety correctly.
- method 2: Dice overlaps of surrounding classes. comparison of mean and std between: 
a) Classes (Global, Ventricles, ...) 
b) samplegoups (all sample pairs, just pairs of contracted and expanded ventricle) 
c) Registration methods (ANTs reference, our diffeomorphic impl, our piecewhise diffeomorpic impl)
- result 2: statistically significant improvement for group of contracted and expanded ventricle pairs
- method 3: healthy to alzheimers patient from ADNI dataset. Inspection of warp grid.},
author = {Nielsen, Rune Kok and Darkner, Sune and Feragen, Aasa},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Nielsen, Darkner, Feragen - 2019 - TopAwaRe Topology-Aware Registration.pdf:pdf},
howpublished = {Unpublished work. Submitted to: $\backslash$textit{\{}International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI){\}}},
journal = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
keywords = {diffeomorphisms,image registration,topology-aware},
pages = {364----372},
title = {{TopAwaRe: Topology-Aware Registration}},
year = {2019}
}
@article{Niethammer2019,
abstract = {Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.},
archivePrefix = {arXiv},
arxivId = {1904.09524},
author = {Niethammer, Marc and Kwitt, Roland and Vialard, Francois-Xavier},
eprint = {1904.09524},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Niethammer, Kwitt, Vialard - 2019 - Metric Learning for Image Registration.pdf:pdf},
journal = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages = {8463--8472},
title = {{Metric Learning for Image Registration}},

year = {2019}
}
@article{Ulman2017,
author = {Ulman, Vladim{\'{i}}r and Ma{\v{s}}ka, Martin and Magnusson, Klas E.G. and Ronneberger, Olaf and Haubold, Carsten and Harder, Nathalie and Matula, Pavel and Matula, Petr and Svoboda, David and Radojevic, Miroslav and Smal, Ihor and Rohr, Karl and Jald{\'{e}}n, Joakim and Blau, Helen M. and Dzyubachyk, Oleh and Lelieveldt, Boudewijn and Xiao, Pengdong and Li, Yuexiang and Cho, Siu Yeung and Dufour, Alexandre C. and Olivo-Marin, Jean Christophe and Reyes-Aldasoro, Constantino C. and Solis-Lemus, Jose A. and Bensch, Robert and Brox, Thomas and Stegmaier, Johannes and Mikut, Ralf and Wolf, Steffen and Hamprecht, Fred A. and Esteves, Tiago and Quelhas, Pedro and Demirel, {\"{O}}mer and Malmstr{\"{o}}m, Lars and Jug, Florian and Tomancak, Pavel and Meijering, Erik and Mu{\~{n}}oz-Barrutia, Arrate and Kozubek, Michal and Ortiz-De-Solorzano, Carlos},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ulman et al. - 2017 - An objective comparison of cell-tracking algorithms.pdf:pdf},

journal = {Nature Methods},
keywords = {Cell migration,Image processing},
number = {12},
pages = {1141--1152},
pmid = {29083403},
publisher = {Nature Publishing Group},
title = {{An objective comparison of cell-tracking algorithms}},

volume = {14},
year = {2017}
}
@article{Murphy2011,
abstract = {EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed. {\textcopyright} 2011 IEEE.},
author = {Murphy, Keelin and {Van Ginneken}, Bram and Reinhardt, Joseph M. and Kabus, Sven and Ding, Kai and Deng, Xiang and Cao, Kunlin and Du, Kaifang and Christensen, Gary E. and Garcia, Vincent and Vercauteren, Tom and Ayache, Nicholas and Commowick, Olivier and Malandain, Grgoire and Glocker, Ben and Paragios, Nikos and Navab, Nassir and Gorbunova, Vladlena and Sporring, Jon and {De Bruijne}, Marleen and Han, Xiao and Heinrich, Mattias P. and Schnabel, Julia A. and Jenkinson, Mark and Lorenz, Cristian and Modat, Marc and McClelland, Jamie R. and Ourselin, Sebastien and Muenzing, Sascha E.A. and Viergever, Max A. and {De Nigris}, Dante and Collins, D. Louis and Arbel, Tal and Peroni, Marta and Li, Rui and Sharp, Gregory C. and Schmidt-Richberg, Alexander and Ehrhardt, Jan and Werner, Ren{\'{e}} and Smeets, Dirk and Loeckx, Dirk and Song, Gang and Tustison, Nicholas and Avants, Brian and Gee, James C. and Staring, Marius and Klein, Stefan and Stoel, Berend C. and Urschler, Martin and Werlberger, Manuel and Vandemeulebroucke, Jef and Rit, Simon and Sarrut, David and Pluim, Josien P.W.},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Murphy et al. - 2011 - Evaluation of registration methods on thoracic CT The EMPIRE10 challenge.pdf:pdf},

journal = {IEEE Transactions on Medical Imaging},
keywords = {Chest,computed tomography,evaluation,registration},
number = {11},
pages = {1901--1920},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Evaluation of registration methods on thoracic CT: The EMPIRE10 challenge}},
volume = {30},
year = {2011}
}
@book{Gjerris2018,
abstract = {Since 2011 it has been mandatory for all new PhD students at the University of Copenhagen to take a course in RCR (Responsible Conduct of Research). The present book will serve as a textbook for the courses held at the Faculty of Science and at the Faculty of Health and Medical Sciences. The book thus aims to give an accessible presentation of what PhD-students are supposed to learn about RCR; to present a clear and consistent terminology; and to focus on the way RCR is dealt with in Denmark and at the University of Copenhagen. The intended readers are from two faculties where the large majority of research falls under the umbrella of the natural sciences, broadly construed. The book can also be of use to other scientific staff at the University.},
author = {Gjerris, Mickey and Jensen, Karsten Klint and H{\o}j, Jeppe Berggreen and Whiteley*, Louise},
booktitle = {RCR - A Danish texbook for courses in Responsible Conduct of Research},

title = {{RCR - A Danish texbook for courses in Responsible Conduct of Research}},

year = {2018}
}
@inproceedings{Ioffe2015,
abstract = {Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82{\%} top-5 test error, exceeding the accuracy of human raters.},
archivePrefix = {arXiv},
arxivId = {1502.03167},
author = {Ioffe, Sergey and Szegedy, Christian},
booktitle = {International Conference on Machine Learning},
eprint = {1502.03167},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Ioffe, Szegedy - 2015 - Batch normalization Accelerating deep network training by reducing internal covariate shift.pdf:pdf},
pages = {448--456},
publisher = {International Machine Learning Society (IMLS)},
title = {{Batch normalization: Accelerating deep network training by reducing internal covariate shift}},
year = {2015}
}
@inproceedings{Gal2016,
abstract = {Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison , Bayesian models offer a mathematically grounded framework to reason about model uncertainty , but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs-extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and non-linearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predic-tive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.},
author = {{Gal, Yarin and Ghahramani}, Zoubin},
booktitle = {International Conference on Machine Learning},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Gal, Yarin and Ghahramani - 2016 - Dropout as a Bayesian Approximation Representing Model Uncertainty in Deep Learning.pdf:pdf},
pages = {1050----1059},
title = {{Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning}},

year = {2016}
}
@inproceedings{Vercauteren2007,
abstract = {We propose a non-parametric diffeomorphic image registration algorithm based on Thirion's demons algorithm. The demons algorithm can be seen as an optimization procedure on the entire space of displacement fields. The main idea of our algorithm is to adapt this procedure to a space of diffeomorphic transformations. In contrast to many diffeomorphic registration algorithms, our solution is computationally efficient since in practice it only replaces an addition of free form deformations by a few compositions. Our experiments show that in addition to being diffeomorphic, our algorithm provides results that are similar to the ones from the demons algorithm but with transformations that are much smoother and closer to the true ones in terms of Jacobians. {\textcopyright} Springer-Verlag Berlin Heidelberg 2007.},
author = {Vercauteren, Tom and Pennec, Xavier and Perchant, Aymeric and Ayache, Nicholas},
booktitle = {Lecture Notes in Computer Science},

file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Vercauteren et al. - 2007 - Non-parametric diffeomorphic image registration with the demons algorithm.pdf:pdf},


number = {PART 2},
pages = {319--326},
pmid = {18044584},
title = {{Non-parametric diffeomorphic image registration with the demons algorithm}},
volume = {4792},
year = {2007}
}
@article{faisalBeg2005,
abstract = {This paper examine the Euler-Lagrange equations for the solution of the large deformation diffeomor-phic metric mapping problem studied in Dupuis et al. (1998) and Trouv{\'{e}} (1995) in which two images I 0 , I 1 are given and connected via the diffeomorphic change of coordinates I 0 • ϕ −1 = I 1 where ϕ = $\phi$ 1 is the end point at t = 1 of curve $\phi$ t , t ∈ [0, 1] satisfying ˙ $\phi$ t = v t ($\phi$ t), t ∈ [0, 1] with $\phi$ 0 = id. The variational problem takes the form argmin v: ˙ $\phi$ t =v t ($\phi$ t) 1 0 v t 2 V dt + I 0 • $\phi$ −1 1 − I 1 2 L 2 , where v t V is an appropriate Sobolev norm on the velocity field v t ({\textperiodcentered}), and the second term enforces matching of the images with {\textperiodcentered}{\textperiodcentered} L 2 representing the squared-error norm. In this paper we derive the Euler-Lagrange equations characterizing the minimizing vector fields v t , t ∈ [0, 1] assuming sufficient smoothness of the norm to guarantee existence of solutions in the space of diffeomorphisms. We describe the implementation of the Euler equations using semi-Lagrangian method of computing particle flows and show the solutions for various examples. We also compute the metric distance on several anatomical configurations as measured by 1 0 v t V dt on the geodesic shortest paths. 140 Beg et al.},
annote = {implementation in cpp: https://github.com/frankyeh/TIPL/blob/master/reg/lddmm.hpp},
author = {{Faisal Beg}, Mirza and Miller, Michael I and Trouv{\'{e}}trouv, Alain and Younes, Laurent},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Faisal Beg et al. - 2005 - Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms.pdf:pdf},
journal = {International Journal of Computer Vision},
keywords = {Euler-Lagrange equation,computational anatomy,deformable template,metrics,variational optimization},
number = {2},
pages = {139--157},
title = {{Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms}},
volume = {61},
year = {2005}
}
@article{Cvpr2020,
author = {Cvpr, Anonymous and Id, Paper},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Cvpr, Id - 2020 - A fast and memory efficient perceptual similarity metric for deep neural networks v0.pdf:pdf},
title = {{A fast and memory efficient perceptual similarity metric for deep neural networks v0}},
year = {2020}
}
@article{Vandemeulebroucke2007,
author = {Vandemeulebroucke, Jef and Sarrut, David and Clarysse, Patrick},
file = {:home/steffen/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Vandemeulebroucke et al. - 2007 - The POPI model, a pointvalidated pixel-based breathing thorax model.pdf:pdf},
journal = {XVth ICCR},
title = {{The POPI model, a pointvalidated pixel-based breathing thorax model}},

year = {2007}
}
@article{citationrequired,
author = {CitationRequired},
title = {{TODO}},
year = {9999}
}
@book{Gjerris2018,
abstract = {Since 2011 it has been mandatory for all new PhD students at the University of Copenhagen to take a course in RCR (Responsible Conduct of Research). The present book will serve as a textbook for the courses held at the Faculty of Science and at the Faculty of Health and Medical Sciences. The book thus aims to give an accessible presentation of what PhD-students are supposed to learn about RCR; to present a clear and consistent terminology; and to focus on the way RCR is dealt with in Denmark and at the University of Copenhagen. The intended readers are from two faculties where the large majority of research falls under the umbrella of the natural sciences, broadly construed. The book can also be of use to other scientific staff at the University.},
author = {Gjerris, Mickey and Jensen, Karsten Klint and H{\o}j, Jeppe Berggreen and Whiteley*, Louise},
booktitle = {RCR - A Danish texbook for courses in Responsible Conduct of Research},

title = {{RCR - A Danish texbook for courses in Responsible Conduct of Research}},

year = {2018}
}
