@article{muller2022instant,
  title={Instant neural graphics primitives with a multiresolution hash encoding},
  author={M{\"u}ller, Thomas and Evans, Alex and Schied, Christoph and Keller, Alexander},
  journal={ACM Transactions on Graphics (ToG)},
  volume={41},
  number={4},
  pages={1--15},
  year={2022},
  publisher={ACM New York, NY, USA}
}
@article{huang2023efficient,
  title={Efficient physics-informed neural networks using hash encoding},
  author={Huang, Xinquan and Alkhalifah, Tariq},
  journal={Journal of Computational Physics},
  pages={112760},
  year={2024},
  publisher={Elsevier}
}

@article{hering2022learn2reg,
  title={Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning},
  author={Hering, Alessa and Hansen, Lasse and Mok, Tony CW and Chung, Albert CS and Siebert, Hanna and H{\"a}ger, Stephanie and Lange, Annkristin and Kuckertz, Sven and Heldmann, Stefan and Shao, Wei and others},
  journal={IEEE Transactions on Medical Imaging},
  year={2022},
  publisher={IEEE}
}
@article{balik2013evaluation,
  title={Evaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy},
  author={Balik, Salim and Weiss, Elisabeth and Jan, Nuzhat and Roman, Nicholas and Sleeman, William C and Fatyga, Mirek and Christensen, Gary E and Zhang, Cheng and Murphy, Martin J and Lu, Jun and others},
  journal={International Journal of Radiation Oncology* Biology* Physics},
  volume={86},
  number={2},
  pages={372--379},
  year={2013},
  publisher={Elsevier}
}

@article{Fu_2020,
doi = {10.1088/1361-6560/ab843e},
url = {https://dx.doi.org/10.1088/1361-6560/ab843e},
year = {2020},
month = {oct},
publisher = {IOP Publishing},
volume = {65},
number = {20},
pages = {20TR01},
author = {Yabo Fu and Yang Lei and Tonghe Wang and Walter J Curran and Tian Liu and Xiaofeng Yang},
title = {Deep learning in medical image registration: a review},
journal = {Physics in Medicine \& Biology},
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 potential. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration.}
}

@article{DEVOS2019128,
title = {A deep learning framework for unsupervised affine and deformable image registration},
journal = {Medical Image Analysis},
volume = {52},
pages = {128-143},
year = {2019},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2018.11.010},
url = {https://www.sciencedirect.com/science/article/pii/S1361841518300495},
author = {Bob D. {de Vos} and Floris F. Berendsen and Max A. Viergever and Hessam Sokooti and Marius Staring and Ivana Išgum},
keywords = {Deep learning, Unsupervised learning, Affine image registration, Deformable image registration, Cardiac cine MRI, Chest CT},
abstract = {Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.}
}

 @article{Fechter_2020,
title={One-Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking},
volume={39},
ISSN={1558-254X},
url={http://dx.doi.org/10.1109/tmi.2020.2972616},
DOI={10.1109/tmi.2020.2972616},
number={7},
journal={IEEE Transactions on Medical Imaging},
publisher={Institute of Electrical and Electronics Engineers (IEEE)}, 
author={Fechter, Tobias and Baltas, Dimos},
year={2020},
month=jul,
pages={2506–2517}
}
@ARTICLE{baum2023,
  author={Baum, Zachary M. C. and Hu, Yipeng and Barratt, Dean C.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Meta-Learning Initializations for Interactive Medical Image Registration}, 
  year={2023},
  volume={42},
  number={3},
  pages={823-833},
  keywords={Image registration;Biomedical imaging;Training;Annotations;Image segmentation;Ultrasonic imaging;Task analysis;Medical image registration;meta-learning;interactive machine learning;prostate cancer},
  doi={10.1109/TMI.2022.3218147}}


@InProceedings{pmlr-v172-wolterink22a,
  title = 	 {Implicit Neural Representations for Deformable Image Registration},
  author =       {Wolterink, Jelmer M and Zwienenberg, Jesse C and Brune, Christoph},
  booktitle = 	 {Proceedings of The 5th International Conference on Medical Imaging with Deep Learning},
  pages = 	 {1349--1359},
  year = 	 {2022},
  volume = 	 {172},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--08 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v172/wolterink22a/wolterink22a.pdf},
  url = 	 {https://proceedings.mlr.press/v172/wolterink22a.html}
}

@article{Nichol2018OnFM,
  title={On First-Order Meta-Learning Algorithms},
  author={Alex Nichol and Joshua Achiam and John Schulman},
  journal={ArXiv},
  year={2018},
  volume={abs/1803.02999},
  url={https://api.semanticscholar.org/CorpusID:4587331}
}

@article{Park2019DeepSDFLC,
  title={{DeepSDF}: Learning Continuous Signed Distance Functions for Shape Representation},
  author={Jeong Joon Park and Peter R. Florence and Julian Straub and Richard A. Newcombe and S. Lovegrove},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={165-174},
  url={https://api.semanticscholar.org/CorpusID:58007025}
}

@inproceedings{45803,
title	= {HyperNetworks},
author	= {David Ha and Andrew Dai and Quoc Le},
booktitle = {Fifth International Conference on Learning Representations (ICLR)},
url={https://openreview.net/forum?id=rkpACe1lx},
year	= {2017}}

@article{babu2023hyperfields,
title={HyperFields: Towards Zero-Shot Generation of NeRFs from Text},
author={Babu, Sudarshan and Liu, Richard and Zhou, Avery and Maire, Michael and Shakhnarovich, Greg and Hanocka, Rana},   
journal={arXiv preprint arXiv:2310.17075},   
year={2023}
}

@article{Castillo_2009,
doi = {10.1088/0031-9155/54/7/001},
url = {https://dx.doi.org/10.1088/0031-9155/54/7/001},
year = {2009},
month = {mar},
publisher = {},
volume = {54},
number = {7},
pages = {1849},
author = {Richard Castillo and Edward Castillo and Rudy Guerra and Valen E Johnson and Travis McPhail and Amit K Garg and Thomas Guerrero},
title = {A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets},
journal = {Physics in Medicine \& Biology},
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 (&gt;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 95% 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.}
}

@article{AberleReduced,
author = {Aberle, Deni and Adams, Amanda and Berg, Christine and Black, William and Clapp, Jonathan and Fagerstrom, Richard and Gareen, Ilana and Gatsonis, Constantine and Marcus, Pamela and Sicks, JoRean},
year = {2011},
month = {08},
pages = {395-409},
title = {Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening},
volume = {365},
journal = {The New England journal of medicine},
doi = {10.1056/NEJMoa1102873}
}

@article{HugoLongitudinal,
author = {Hugo, Geoffrey and Weiss, Elisabeth and Sleeman, William and Balik, Salim and Keall, Paul and Lu, Jun and Williamson, Jeff},
year = {2016},
month = {12},
pages = {},
title = {A longitudinal four‐dimensional computed tomography and cone beam computed tomography dataset for image‐guided radiation therapy research in lung cancer},
volume = {44},
journal = {Medical Physics},
doi = {10.1002/mp.12059}
}

@inproceedings{sitzmann2019siren,
                author = {Sitzmann, Vincent
                          and Martel, Julien N.P.
                          and Bergman, Alexander W.
                          and Lindell, David B.
                          and Wetzstein, Gordon},
                title = {Implicit Neural Representations
                          with Periodic Activation Functions},
                booktitle = {Proc. NeurIPS},
                year={2020}
            }

@article{vanharten2023robust,
  author={van Harten, Louis D. and Stoker, Jaap and Išgum, Ivana},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Robust Deformable Image Registration Using Cycle-Consistent Implicit Representations}, 
  year={2024},
  volume={43},
  number={2},
  pages={784-793},
  keywords={Lung;Image registration;Magnetic resonance imaging;Deformation;Computed tomography;Uncertainty;Measurement;Deformable image registration;implicit neural representations;quality control;regularization},
  doi={10.1109/TMI.2023.3321425}
}

@article{hofmanninger2020automatic,
  title={Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem},
  author={Hofmanninger, Johannes and Prayer, Forian and Pan, Jeanny and R{\"o}hrich, Sebastian and Prosch, Helmut and Langs, Georg},
  journal={European Radiology Experimental},
  volume={4},
  number={1},
  pages={1--13},
  year={2020},
  publisher={SpringerOpen},
  doi={10.1186/s41747-020-00173-2}
}

@article{papa2023neural,
  title={Neural Modulation Fields for Conditional Cone Beam Neural Tomography},
  author={Papa, Samuele and Knigge, David M and Valperga, Riccardo and Moriakov, Nikita and Kofinas, Miltos and Sonke, Jan-Jakob and Gavves, Efstratios},
  journal={arXiv preprint arXiv:2307.08351},
  year={2023}
}

@inproceedings{jia2022u,
  title={U-net vs transformer: Is u-net outdated in medical image registration?},
  author={Jia, Xi and Bartlett, Joseph and Zhang, Tianyang and Lu, Wenqi and Qiu, Zhaowen and Duan, Jinming},
  booktitle={International Workshop on Machine Learning in Medical Imaging},
  pages={151--160},
  year={2022},
  organization={Springer}
}
@inproceedings{he2015delving,
  title={Delving deep into rectifiers: Surpassing human-level performance on imagenet classification},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1026--1034},
  year={2015}
}

@inproceedings{tancik2021learned,
  title={Learned initializations for optimizing coordinate-based neural representations},
  author={Tancik, Matthew and Mildenhall, Ben and Wang, Terrance and Schmidt, Divi and Srinivasan, Pratul P and Barron, Jonathan T and Ng, Ren},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2846--2855},
  year={2021}
}

@InProceedings{perez2018film,
  title={FiLM: Visual Reasoning with a General Conditioning Layer},
  author={Ethan Perez and Florian Strub and Harm de Vries and Vincent Dumoulin and Aaron C. Courville},
  booktitle={AAAI},
  year={2018}
}

@InProceedings{functa22,
  title = {From data to functa: Your data point is a function and you can treat it like one},
  author = {Dupont, Emilien and Kim, Hyunjik and Eslami, S. M. Ali and Rezende, Danilo Jimenez and Rosenbaum, Dan},
  booktitle = {39th International Conference on Machine Learning (ICML)},
  year = {2022},
}

@article{balakrishnan2019voxelmorph,
  title={Voxelmorph: a learning framework for deformable medical image registration},
  author={Balakrishnan, Guha and Zhao, Amy and Sabuncu, Mert R and Guttag, John and Dalca, Adrian V},
  journal={IEEE transactions on medical imaging},
  volume={38},
  number={8},
  pages={1788--1800},
  year={2019},
  publisher={IEEE}
}
@article{dalca2019unsupervised,
  title={Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces},
  author={Dalca, Adrian V and Balakrishnan, Guha and Guttag, John and Sabuncu, Mert R},
  journal={Medical image analysis},
  volume={57},
  pages={226--236},
  year={2019},
  publisher={Elsevier}
}
