@InProceedings{C2,
  author = 	 "Jones, C.D. and Smith, A.B. and Roberts, E.F.",
  title =        "Paper Title",
  booktitle =    "Proceedings Title",
  organization = "IEEE",
  year = 	 "2003",
  volume = 	 "II",
  pages = 	 "803-806"
}

@article{falkner-icml-18,
  title =        {{BOHB}: Robust and Efficient Hyperparameter Optimization at Scale},
  author =       {Falkner, Stefan and Klein, Aaron and Hutter, Frank},
  journal =    {Proceedings of the 35th International Conference on Machine Learning},
  pages =        {1436--1445},
  year =         {2018},
}

@article{butoi2023universeg,
  title={UniverSeg: Universal Medical Image Segmentation},
  author={Victor Ion Butoi and Jose Javier Gonzalez Ortiz and Tianyu Ma and Mert R. Sabuncu and John Guttag and Adrian V. Dalca},
  journal={International Conference on Computer Vision},
  year={2023}
}

@inproceedings{babaiee2024neural,
  title={Neural echos: Depthwise convolutional filters replicate biological receptive fields},
  author={Babaiee, Zahra and Kiasari, Peyman M and Rus, Daniela and Grosu, Radu},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={8216--8225},
  year={2024}
}

@article{maier2024metrics,
  title={Metrics reloaded: recommendations for image analysis validation},
  author={Maier-Hein, Lena and Reinke, Annika and Godau, Patrick and Tizabi, Minu D and Buettner, Florian and Christodoulou, Evangelia and Glocker, Ben and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and others},
  journal={Nature methods},
  pages={1--18},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@ARTICLE{ssnHuo2019,
  title     = "Splenomegaly segmentation on multi-modal {MRI} using deep
               convolutional networks",
  author    = "Huo, Yuankai and Xu, Zhoubing and Bao, Shunxing and Bermudez,
               Camilo and Moon, Hyeonsoo and Parvathaneni, Prasanna and Moyo,
               Tamara K and Savona, Michael R and Assad, Albert and Abramson,
               Richard G and Landman, Bennett A",
  journal   = "IEEE Trans. Med. Imaging",
  volume    =  38,
  number    =  5,
  pages     = "1185--1196",
  month     =  may,
  year      =  2019,
}

@article{limbuswong2020,
title={Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning},
author={Wong, Jordan and Fong, Allan and McVicar, Nevin and Smith, Sally and Giambattista, Joshua and Wells, Derek and Kolbeck, Carter and Giambattista, Jonathan and Gondara, Lovedeep and Alexander, Abraham},
journal={Radiotherapy and Oncology},
volume={144},
pages={152--158},
year={2020},
publisher={Elsevier},
url={https://limbus.ai},
}

@ARTICLE{TherapanaceaEstienne2020,
title    = "Deep {Learning-Based} Concurrent Brain Registration and Tumor
          Segmentation",
author   = "Estienne, Th{\'e}o and Lerousseau, Marvin and Vakalopoulou, Maria
          and Alvarez Andres, Emilie and Battistella, Enzo and Carr{\'e},
          Alexandre and Chandra, Siddhartha and Christodoulidis, Stergios
          and Sahasrabudhe, Mihir and Sun, Roger and Robert, Charlotte and
          Talbot, Hugues and Paragios, Nikos and Deutsch, Eric",
abstract = "Image registration and segmentation are the two most studied
          problems in medical image analysis. Deep learning algorithms have
          recently gained a lot of attention due to their success and
          state-of-the-art results in variety of problems and communities.
          In this paper, we propose a novel, efficient, and multi-task
          algorithm that addresses the problems of image registration and
          brain tumor segmentation jointly. Our method exploits the
          dependencies between these tasks through a natural coupling of
          their interdependencies during inference. In particular, the
          similarity constraints are relaxed within the tumor regions using
          an efficient and relatively simple formulation. We evaluated the
          performance of our formulation both quantitatively and
          qualitatively for registration and segmentation problems on two
          publicly available datasets (BraTS 2018 and OASIS 3), reporting
          competitive results with other recent state-of-the-art methods.
          Moreover, our proposed framework reports significant amelioration
          (p < 0.005) for the registration performance inside the tumor
          locations, providing a generic method that does not need any
          predefined conditions (e.g., absence of abnormalities) about the
          volumes to be registered. Our implementation is publicly
          available online at
          https://github.com/TheoEst/joint\_registration\_tumor\_segmentation.",
journal  = "Front Comput Neurosci",
volume   =  14,
pages    = "17",
month    =  mar,
year     =  2020,
address  = "Switzerland",
keywords = "brain tumor segmentation; convolutional neural networks; deep
          learning; deformable registration; multi-task networks",
language = "en",
url="https://github.com/TheoEst/joint\_registration\_tumor\_segmentation",
}

@misc{btcv_gibson_eli_2018,
  author       = {Gibson, Eli and
                  Giganti, Francesco and
                  Hu, Yipeng and
                  Bonmati, Ester and
                  Bandula, Steve and
                  Gurusamy, Kurinchi and
                  Davidson, Brian and
                  Pereira, Stephen P. and
                  Clarkson, Matthew J. and
                  Barratt, Dean C.},
  title        = {{Multi-organ Abdominal CT Reference Standard 
                   Segmentations}},
  month        = feb,
  year         = 2018,
  publisher    = {Zenodo},
  version      = {1.0},
  url          = {https://doi.org/10.5281/zenodo.1169361}
}

@article{roy2023mednext,
  title={Mednext: transformer-driven scaling of convnets for medical image segmentation},
  author={Roy, Saikat and Koehler, Gregor and Ulrich, Constantin and Baumgartner, Michael and Petersen, Jens and Isensee, Fabian and Jaeger, Paul F and Maier-Hein, Klaus H},
  journal={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={405--415},
  year={2023},
  organization={Springer}
}

@article{MARHOLD2022304,
title = {The prostate cancer landscape in Europe: Current challenges, future opportunities},
journal = {Cancer Letters},
volume = {526},
pages= {304-310},
year = {2022},
author = {Maximilian Marhold and Gero Kramer and Michael Krainer and Clémentine {Le Magnen}},
keywords = {Prostate cancer, Treatment, Europe},
abstract = {Prostate cancer (PCa) is the most common non-cutaneous cancer in men in Europe and is predicted to exhibit declining mortality in the European Union (EU) due to various recent improvements in treatment. The goal of this short review is to give insight into the European treatment landscape of PCa, while focusing on improvements in care.}
}

@misc{saha_anindo_2022_6522364,
author       = {Saha, Anindo and
              Twilt, Jasper Jonathan and
              Bosma, Joeran Sander and
              van Ginneken, Bram and
              Yakar, Derya and
              Elschot, Mattijs and
              Veltman, Jeroen and
              Fütterer, Jurgen and
              de Rooij, Maarten and
              Huisman, Henkjan},
title        = {{Artificial Intelligence and Radiologists at 
               Prostate Cancer Detection in MRI: The PI-CAI
               Challenge (Study Protocol)}},
month        = may,
year         = 2022,
publisher    = {Zenodo},
version      = {1.0},
doi          = {10.5281/zenodo.6522364},
url          = {https://pi-cai.grand-challenge.org}
}


@article{BHANDARY2023102241,
title = {Investigation and benchmarking of U-Nets on prostate segmentation tasks},
journal = {Computerized Medical Imaging and Graphics},
volume = {107},
pages = {102241},
year = {2023},
author = {Shrajan Bhandary and Dejan Kuhn and Zahra Babaiee and Tobias Fechter and Matthias Benndorf and Constantinos Zamboglou and Anca-Ligia Grosu and Radu Grosu},
keywords = {Medical imaging, Automatic prostate segmentation, U-net variations, Comparison framework},
}

@article {nnU_Net_2020, 	
    Title = {nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation}, 	
    Author = {Isensee, Fabian and Jaeger, Paul F and Kohl, Simon A A and Petersen, Jens and Maier-Hein, Klaus H}, 	
    Number = {2}, 	
    Volume = {18}, 	
    Month = {2}, 	
    Year = {2021}, 	
    Journal = {Nature methods}, 	
    Pages = {203—211}, 	
    Abstract = {Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning. While semantic segmentation algorithms enable image analysis and quantification in many applications, the design of respective specialized solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We developed nnU-Net, a deep learning-based segmentation method that automatically configures itself, including preprocessing, network architecture, training and post-processing for any new task. The key design choices in this process are modeled as a set of fixed parameters, interdependent rules and empirical decisions. Without manual intervention, nnU-Net surpasses most existing approaches, including highly specialized solutions on 23 public datasets used in international biomedical segmentation competitions. We make nnU-Net publicly available as an out-of-the-box tool, rendering state-of-the-art segmentation accessible to a broad audience by requiring neither expert knowledge nor computing resources beyond standard network training.}, 	
}  

@article{attention_unet_2018,
    author    = {Ozan Oktay and
               Jo Schlemper and
               Lo{\"{\i}}c Le Folgoc and
               Matthew C. H. Lee and
               Mattias P. Heinrich and
               Kazunari Misawa and
               Kensaku Mori and
               Steven G. McDonagh and
               Nils Y. Hammerla and
               Bernhard Kainz and
               Ben Glocker and
               Daniel Rueckert},
    title     = {Attention U-Net: Learning Where to Look for the Pancreas},
    journal   = {Medical Imaging with Deep Learning},
    year      = {2018},
   
}

@article {karimi_IJCARS_2018, 	
    Title = {Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models}, 	
    Author = {Karimi, Davood and Samei, Golnoosh and Kesch, Claudia and Nir, Guy and Salcudean, Septimiu E}, 	
    Number = {8}, 	
    Volume = {13}, 	
    Month = {8}, 	
    Year = {2018}, 	
    Journal = {International journal of computer assisted radiology and surgery}, 	
    Pages = {1211—1219}, 	
    Abstract = {PURPOSE:Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. METHODS:Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. RESULTS:Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. CONCLUSIONS:Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.}, 	
}  

@article {zeng_IJCARS_2018, 	
    Title = {Prostate segmentation in transrectal ultrasound using magnetic resonance imaging priors}, 	
    Author = {Zeng, Qi and Samei, Golnoosh and Karimi, Davood and Kesch, Claudia and Mahdavi, Sara S and Abolmaesumi, Purang and Salcudean, Septimiu E}, 
    Number = {6}, 	Volume = {13}, 	Month = {6}, 	Year = {2018}, 	
    Journal = {International journal of computer assisted radiology and surgery}, 	Pages = {749—757}, 	
    Abstract = {&lt;h4&gt;Purpose&lt;/h4&gt;In the current standard of care, real-time transrectal ultrasound (TRUS) is commonly used for prostate brachytherapy guidance. As TRUS provides limited soft tissue contrast, segmenting the prostate gland in TRUS images is often challenging and subject to inter-observer and intra-observer variability, especially at the base and apex where the gland boundary is hard to define. Magnetic resonance imaging (MRI) has higher soft tissue contrast allowing the prostate to be contoured easily. In this paper, we aim to show that prostate segmentation in TRUS images informed by MRI priors can improve on prostate segmentation that relies only on TRUS images.&lt;h4&gt;Methods&lt;/h4&gt;First, we compare the TRUS-based prostate segmentation used in the treatment of 598 patients with a high-quality MRI prostate atlas and observe inconsistencies at the apex and base. Second, motivated by this finding, we propose an alternative TRUS segmentation technique that is fully automatic and uses MRI priors. The algorithm uses a convolutional neural network to segment the prostate in TRUS images at mid-gland, where the gland boundary can be clearly seen. It then reconstructs the gland boundary at the apex and base with the aid of a statistical shape model built from an MRI atlas of 78 patients.&lt;h4&gt;Results&lt;/h4&gt;Compared to the clinical TRUS segmentation, our method achieves similar mid-gland segmentation results in the 598-patient database. For the seven patients who had both TRUS and MRI, our method achieved more accurate segmentation of the base and apex with the MRI segmentation used as ground truth.&lt;h4&gt;Conclusion&lt;/h4&gt;Our results suggest that utilizing MRI priors in TRUS prostate segmentation could potentially improve the performance at base and apex.}, 	
}  

@ARTICLE{BowdaNet_Zhu2019, 
author={Q. {Zhu} and B. {Du} and P. {Yan}}, 
journal={IEEE Transactions on Medical Imaging}, 
title={Boundary-weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation}, 
year={2019},
}

@misc{qin2019transfer,
title={Transfer learning with edge attention for prostate MRI segmentation},
author={Qin, Xiangxiang},
journal={arXiv preprint arXiv:1912.09847},
year={2019}
}

@InProceedings{HDNet_Haozhe2019,
author="Jia, Haozhe
and Song, Yang
and Huang, Heng
and Cai, Weidong
and Xia, Yong",
editor="Shen, Dinggang
and Liu, Tianming
and Peters, Terry M.
and Staib, Lawrence H.
and Essert, Caroline
and Zhou, Sean
and Yap, Pew-Thian
and Khan, Ali",
title="HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="110--118",
abstract="Efficient and accurate segmentation of prostate gland facilitates the prediction of the pathologic stage and treatment response. Recently, deep learning methods have been proposed to tackle this issue. However, the effectiveness of these methods is often limited by inadequate semantic discrimination and spatial context modeling. To address these issues, we propose the Hybrid Discriminative Network (HD-Net), which consists of a 3D segmentation decoder using channel attention block to generate semantically consistent volumetric features and an auxiliary 2D boundary decoder guiding the segmentation network to focus on the semantically discriminative intra-slice features. Meanwhile, we further design the pyramid convolution block and residual refinement block for HD-Net to fully exploit multi-scale spatial contextual information of the prostate gland. In addition, to reduce the information loss in propagation and fully fuse the multi-scale feature maps, we introduce inter-scale dense shortcuts for both decoders. We evaluated our model on the Prostate MR Image Segmentation 2012 (PROMISE12) challenge dataset and achieved a synthetic score of 90.34, setting a new state of the art.",
}

@inproceedings{isensee2023extending,
  title={Extending nnU-Net is all you need},
  author={Isensee, Fabian and Ulrich, Constantin and Wald, Tassilo and Maier-Hein, Klaus H},
  booktitle={BVM workshop},
  pages={12--17},
  year={2023},
  organization={Springer}
}

@ARTICLE{prostatex_Litjens_2014,
  author={Litjens, Geert and Debats, Oscar and Barentsz, Jelle and Karssemeijer, Nico and Huisman, Henkjan},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Computer-Aided Detection of Prostate Cancer in MRI}, 
  year={2014},
  volume={33},
  number={5},
  pages={1083-1092}}

@misc{ulrich2023multitalent,
  title={MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation},
  author={Ulrich, Constantin and Isensee, Fabian and Wald, Tassilo and Zenk, Maximilian and Baumgartner, Michael and Maier-Hein, Klaus H},
  journal={arXiv preprint arXiv:2303.14444},
  year={2023}
}

@article{MSD_NET_Haozhe2022,
title = {Learning multi-scale synergic discriminative features for prostate image segmentation},
journal = {Pattern Recognition},
volume = {126},
pages= {108556},
year = {2022},
author = {Haozhe Jia and Weidong Cai and Heng Huang and Yong Xia},
keywords = {Prostate segmentation, Intra-class consistency, Inter-class discrimination, Synergic multi-task loss},
abstract = {Although deep convolutional neural networks (DCNNs) have been proposed for prostate MR image segmentation, the effectiveness of these methods is often limited by inadequate semantic discrimination and spatial context modeling. To address these issues, we propose a Multi-scale Synergic Discriminative Network (MSD-Net), which includes a shared encoder, a segmentation decoder, and a boundary detection decoder. We further design the cascaded pyramid convolutional block and residual refinement block, and incorporate them and the channel attention block into MSD-Net to exploit the multi-scale spatial contextual information and semantically consistent features of the gland. We also fuse the features from two decoders to boost the segmentation performance, and introduce the synergic multi-task loss to impose the consistence constraint on the joint segmentation and boundary detection. We evaluated MSD-Net against several prostate segmentation methods on three public datasets and achieved an improved accuracy. Our results indicate that the proposed MSD-Net outperforms existing methods with setting the new state-of-the-art for prostate segmentation in magnetic resonance images.}
}

@misc{ji2022amos,
  title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation},
  author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others},
  journal={arXiv preprint arXiv:2206.08023},
  url={https://amos22.grand-challenge.org},
  year={2022}
}


@article{zhao_EJNMMI_2020,
   Author="Zhao, Y.  and Gafita, A.  and Vollnberg, B.  and Tetteh, G.  and Haupt, F.  and Afshar-Oromieh, A.  and Menze, B.  and Eiber, M.  and Rominger, A.  and Shi, K. ",
   Title="{{D}eep neural network for automatic characterization of lesions on 68{G}a-{P}{S}{M}{A}-11 {P}{E}{T}/{C}{T}}",
   Journal="Eur J Nucl Med Mol Imaging",
   Year="2020",
   Volume="47",
   Number="3",
   Pages="603--613",
   Month="03"
}


@Article{3d_survey_singh_2020,
    AUTHOR = {Singh, Satya P. and Wang, Lipo and Gupta, Sukrit and Goli, Haveesh and Padmanabhan, Parasuraman and Gulyás, Balázs},
    TITLE = {3D Deep Learning on Medical Images: A Review},
    JOURNAL = {Sensors},
    VOLUME = {20},
    YEAR = {2020},
    NUMBER = {18},
    ARTICLE-NUMBER = {5097},
    ABSTRACT = {The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.},
    
}

@Article{goldenberg_NATURE_2019,
    author={Goldenberg, S. Larry
    and Nir, Guy
    and Salcudean, Septimiu E.},
    title={A new era: artificial intelligence and machine learning in prostate cancer},
    journal={Nature Reviews Urology},
    year={2019},
    month={7},
    day={01},
    volume={16},
    number={7},
    pages={391-403},
    abstract={Artificial intelligence (AI) --- the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data --- is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to `big data' enables the `cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.},
}

@article{vnet_2016,
  title={V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation},
  author={Fausto Milletari and Nassir Navab and Seyed-Ahmad Ahmadi},
  journal={2016 Fourth International Conference on 3D Vision (3DV)},
  year={2016},
  pages={565-571}
}

@article{inception_2015,
  author={Szegedy, Christian and Wei Liu and Yangqing Jia and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={Going deeper with convolutions}, 
  year={2015},
  volume={},
  number={},
  pages={1-9},
  doi={10.1109/CVPR.2015.7298594}
}

@article{zhou2018unetpp,
  title={Unet++: A nested u-net architecture for medical image segmentation},
  author={Zhou, Zongwei and Rahman Siddiquee, Md Mahfuzur and Tajbakhsh, Nima and Liang, Jianming},
  Journal={Deep learning in medical image analysis and multimodal learning for clinical decision support},
  pages={3-11},
  year={2018},
  publisher={Springer}
}

@Article{steenbergen_2015,
title = {Prostate tumor delineation using multiparametric magnetic resonance imaging: Inter-observer variability and pathology validation},
journal = {Radiotherapy and Oncology},
volume = {115},
number = {2},
pages = {186-190},
year = {2015},
author = {Peter Steenbergen and Karin Haustermans and Evelyne Lerut and Raymond Oyen and Liesbeth {De Wever} and Laura {Van den Bergh} and Linda G.W. Kerkmeijer and Frank A. Pameijer and Wouter B. Veldhuis and Jochem R.N. {van der Voort van Zyp} and Floris J. Pos and Stijn W. Heijmink and Robin Kalisvaart and Hendrik J. Teertstra and Cuong V. Dinh and Ghazaleh Ghobadi and Uulke A. {van der Heide}},
keywords = {Prostate cancer, Radiotherapy, Tumor delineation, Multiparametric MRI, Dose escalation, Inter-observer variability},
}

@article{GHAVAMI2019101558,
title = {Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration},
journal = {Medical Image Analysis},
volume = {58},
year = {2019},
author = {Nooshin Ghavami and Yipeng Hu and Eli Gibson and Ester Bonmati and Mark Emberton and Caroline M. Moore and Dean C. Barratt},
keywords = {Medical image segmentation, Neural networks, Prostate cancer, MRI},
abstract = {Convolutional neural networks (CNNs) have recently led to significant advances in automatic segmentations of anatomical structures in medical images, and a wide variety of network architectures are now available to the research community. For applications such as segmentation of the prostate in magnetic resonance images (MRI), the results of the PROMISE12 online algorithm evaluation platform have demonstrated differences between the best-performing segmentation algorithms in terms of numerical accuracy using standard metrics such as the Dice score and boundary distance. These small differences in the segmented regions/boundaries outputted by different algorithms may potentially have an unsubstantial impact on the results of downstream image analysis tasks, such as estimating organ volume and multimodal image registration, which inform clinical decisions. This impact has not been previously investigated. In this work, we quantified the accuracy of six different CNNs in segmenting the prostate in 3D patient T2-weighted MRI scans and compared the accuracy of organ volume estimation and MRI-ultrasound (US) registration errors using the prostate segmentations produced by different networks. Networks were trained and tested using a set of 232 patient MRIs with labels provided by experienced clinicians. A statistically significant difference was found among the Dice scores and boundary distances produced by these networks in a non-parametric analysis of variance (p < 0.001 and p < 0.001, respectively), where the following multiple comparison tests revealed that the statistically significant difference in segmentation errors were caused by at least one tested network. Gland volume errors (GVEs) and target registration errors (TREs) were then estimated using the CNN-generated segmentations. Interestingly, there was no statistical difference found in either GVEs or TREs among different networks, (p = 0.34 and p = 0.26, respectively). This result provides a real-world example that these networks with different segmentation performances may potentially provide indistinguishably adequate registration accuracies to assist prostate cancer imaging applications. We conclude by recommending that the differences in the accuracy of downstream image analysis tasks that make use of data output by automatic segmentation methods, such as CNNs, within a clinical pipeline should be taken into account when selecting between different network architectures, in addition to reporting the segmentation accuracy.}
}

@Article{rischke_2013,
author={Rischke, Hans Christian
and Nestle, Ursula
and Fechter, Tobias
and Doll, Christian
and Volegova-Neher, Natalja
and Henne, Karl
and Scholber, Jutta
and Knippen, Stefan
and Kirste, Simon
and Grosu, Anca L.
and Jilg, Cordula A.},
title={3 Tesla multiparametric MRI for GTV-definition of Dominant Intraprostatic Lesions in patients with Prostate Cancer--an interobserver variability study},
journal={Radiation oncology (London, England)},
year={2013},
month={Jul},
day={22},
publisher={BioMed Central},
volume={8},
pages={183-183},
keywords={Aged; Aged, 80 and over; Humans; Image Interpretation, Computer-Assisted/*methods; Magnetic Resonance Imaging/*methods; Male; Middle Aged; Observer Variation; Prostatic Neoplasms/*pathology; Radiotherapy Planning, Computer-Assisted},
abstract={PURPOSE: To evaluate the interobserver variability of gross tumor volume (GTV) - delineation of Dominant Intraprostatic Lesions (DIPL) in patients with prostate cancer using published MRI criteria for multiparametric MRI at 3 Tesla by 6 different observers. MATERIAL AND METHODS: 90 GTV-datasets based on 15 multiparametric MRI sequences (T2w, diffusion weighted (DWI) and dynamic contrast enhanced (DCE)) of 5 patients with prostate cancer were generated for GTV-delineation of DIPL by 6 observers. The reference GTV-dataset was contoured by a radiologist with expertise in diagnostic imaging of prostate cancer using MRI. Subsequent GTV-delineation was performed by 5 radiation oncologists who received teaching of MRI-features of primary prostate cancer before starting contouring session. GTV-datasets were contoured using Oncentra Masterplan® and iplan® Net. For purposes of comparison GTV-datasets were imported to the Artiview® platform (Aquilab®), GTV-values and the similarity indices or Kappa indices (KI) were calculated with the postulation that a KI{\thinspace}>{\thinspace}0.7 indicates excellent, a KI > 0.6 to < 0.7 substantial and KI > 0.5 to < 0.6 moderate agreement. Additionally all observers rated difficulties of contouring for each MRI-sequence using a 3 point rating scale (1{\thinspace}={\thinspace}easy to delineate, 2{\thinspace}={\thinspace}minor difficulties, 3{\thinspace}={\thinspace}major difficulties). RESULTS: GTV contouring using T2w (KI-T2w{\thinspace}={\thinspace}0.61) and DCE images (KI-DCE{\thinspace}={\thinspace}0.63) resulted in substantial agreement. GTV contouring using DWI images resulted in moderate agreement (KI-DWI{\thinspace}={\thinspace}0.51). KI-T2w and KI-DCE was significantly higher than KI-DWI (p{\thinspace}={\thinspace}0.01 and p{\thinspace}={\thinspace}0.003). Degree of difficulty in contouring GTV was significantly lower using T2w and DCE compared to DWI-sequences (both p{\thinspace}<{\thinspace}0.0001). Analysis of delineation differences revealed inadequate comparison of functional (DWI, DCE) to anatomical sequences (T2w) and lack of awareness of non-specific imaging findings as a source of erroneous delineation. CONCLUSIONS: Using T2w and DCE sequences at 3 Tesla for GTV-definition of DIPL in prostate cancer patients by radiation oncologists with knowledge of MRI features results in substantial agreement compared to an experienced MRI-radiologist, but for radiotherapy purposes higher KI are desirable, strengthen the need for expert surveillance. DWI sequence for GTV delineation was considered as difficult in application.},
language={eng}
}



@Article{brown_2015_imaging,
   Author="Brown, A. M.  and Elbuluk, O.  and Mertan, F.  and Sankineni, S.  and Margolis, D. J.  and Wood, B. J.  and Pinto, P. A.  and Choyke, P. L.  and Turkbey, B. ",
   Title="{{R}ecent advances in image-guided targeted prostate biopsy}",
   Journal="Abdom Imaging",
   Year="2015",
   Volume="40",
   Number="6",
   Pages="1788--1799",
   Month="8"
}

@article{rivas_EMJU_2017,
    author = {Rivas, Juan and Alvarez-Maestro, Mario and Czarniecki, Marcin and Czarniecki, S. and Socarras, Moises and Loeb, Stacy},
    year = {2017},
    month = {05},
    pages = {76-82},
    title = {Negative Biopsies with Rising Prostate-Specific Antigen. What To Do?},
    volume = {5},
    journal = {European Medical Journal Urology}
}

@article{alexnet_2012,
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E.},
title = {ImageNet Classification with Deep Convolutional Neural Networks},
year = {2012},
abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.},
journal = {Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1},
pages = {1097–1105},
numpages = {9},
location = {Lake Tahoe, Nevada},
}

@article{litjens_2017,
    title = {A survey on deep learning in medical image analysis},
    journal = {Medical Image Analysis},
    volume = {42},
    pages = {60-88},
    year = {2017},
    author = {Geert Litjens and Thijs Kooi and Babak Ehteshami Bejnordi and Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Mohsen Ghafoorian and Jeroen A.W.M. {van der Laak} and Bram {van Ginneken} and Clara I. Sánchez},
    keywords = {Deep learning, Convolutional neural networks, Medical imaging, Survey},
    abstract = {Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.}
}

@article{6100395,
  author={Chang, Qing and Bin Zhang and Ruixiang Liu},
  journal={2011 4th International Congress on Image and Signal Processing}, 
  title={Texture analysis method for shape-based segmentation in medical image}, 
  year={2011},
  volume={3},
  number={0},
  pages={1146-1149},
  doi={10.1109/CISP.2011.6100395}
  }


@article{Balagopal_2018,
	doi = {10.1088/1361-6560/aaf11c},
	year = 2018,
	month = {12},
	publisher = {{IOP} Publishing},
	volume = {63},
	number = {24},
	pages = {245015},
	author = {Anjali Balagopal and Samaneh Kazemifar and Dan Nguyen and Mu-Han Lin and Raquibul Hannan and Amir Owrangi and Steve Jiang},
	title = {Fully automated organ segmentation in male pelvic {CT} images},
	journal = {Physics in Medicine {\&} Biology},
	abstract = {Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D organ volume localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (±SD) Dice coefficient values of 90 (±2.0)\%, 96 (±3.0)\%, 95 (±1.3)\%, 95 (±1.5)\%, and 84 (±3.7)\% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.}
}

@article{pytorch_2019_NIPS,
    title={Pytorch: An imperative style, high-performance deep learning library},
  author={Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and others},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}
}

@article{adam_optimizer_2015,
  author    = {Diederik P. Kingma and
               Jimmy Ba},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {Adam: {A} Method for Stochastic Optimization},
  journal = {International Conference on Learning Representations},
  year      = {2015},
  timestamp = {Thu, 25 Jul 2019 14:25:37 +0200},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{cosine_annealing_2017,
  author    = {Ilya Loshchilov and
               Frank Hutter},
  title     = {Fixing Weight Decay Regularization in Adam},
  journal   = {International Conference on Learning Representations},
  year      = {2018},
}

@misc(american_cancer_society,
author = {American Cancer Society},
title = {Key Statistics for Prostate Cancer - Prostate Cancer Facts},
month        = "1",
year         = "2022",
url = "https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html",
)

@misc(american_cancer_society_full,
author = {AmericanCancerSociety},
title = {Key Statistics for Prostate Cancer - Prostate Cancer Facts},
howpublished="\url{https://www.cancer.org/cancer/prostate-cancer/about/key-statistics.html#written_by}",
month        = "1",
year         = "2021",
note="Accessed: 2022-01-06",
)

@online{noauthor_american_cancer_society_nodate,
    author = {American Cancer Society},
	title = {Key Statistics for Prostate Cancer | Prostate Cancer Facts},
	file = {Key Statistics for Prostate Cancer \| Prostate Cancer Facts Format:/home/anonymous/snap/zotero-snap/common/Zotero/storage/D7SNQ94X/format.html:text/html},
}

@article{ioffe_batch_norm_2015,
  title = 	 {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
  author = 	 {Ioffe, Sergey and Szegedy, Christian},
  journal = 	 {Proceedings of the 32nd International Conference on Machine Learning},
  pages = 	 {448-456},
  year = 	 {2015},
  editor = 	 {Bach, Francis and Blei, David},
  volume = 	 {37},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Lille, France},
  month = 	 {7},
  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 stateof-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.}
}



@article{ronneberger_u_net_2015,
	location = {Cham},
	title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	series = {Lecture Notes in Computer Science},
	shorttitle = {U-Net},
	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 .},
	pages = {234-241},
	journal = {Medical Image Computing and Computer-Assisted Intervention – {MICCAI} 2015},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.},
	year = {2015},
	langid = {english},
	keywords = {Training Image, Convolutional Layer, Data Augmentation, Deep Network, Ground Truth Segmentation},
}


@article{cicek_3d_2016,
  title={3D U-Net: learning dense volumetric segmentation from sparse annotation},
  author={{\c{C}}i{\c{c}}ek, {\"O}zg{\"u}n and Abdulkadir, Ahmed and Lienkamp, Soeren S and Brox, Thomas and Ronneberger, Olaf},
  journal={International conference on medical image computing and computer-assisted intervention},
  pages={424--432},
  year={2016},
}

@article{perez-garcia_torchio_2021,
   title = {TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
journal = {Computer Methods and Programs in Biomedicine},
volume = {208},
pages = {106236},
year = {2021},
author = {Fernando Pérez-García and Rachel Sparks and Sébastien Ourselin},
keywords = {Medical image computing, Deep learning, Data augmentation, Preprocessing},
abstract = {Background and objective
Processing of medical images such as MRI or CT presents different challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and the need of metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image subvolumes or patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment and orientation of volumes.
Methods
We present TorchIO, an open-source Python library to enable efficient loading, preprocessing, augmentation and patch-based sampling of medical images for deep learning. TorchIO follows the style of PyTorch and integrates standard medical image processing libraries to efficiently process images during training of neural networks. TorchIO transforms can be easily composed, reproduced, traced and extended. Most transforms can be inverted, making the library suitable for test-time augmentation and estimation of aleatoric uncertainty in the context of segmentation. We provide multiple generic preprocessing and augmentation operations as well as simulation of MRI-specific artifacts.
Results
Source code, comprehensive tutorials and extensive documentation for TorchIO can be found at http://torchio.rtfd.io/. The package can be installed from the Python Package Index (PyPI) running pip install torchio. It includes a command-line interface which allows users to apply transforms to image files without using Python. Additionally, we provide a graphical user interface within a TorchIO extension in 3D Slicer to visualize the effects of transforms.
Conclusion
TorchIO was developed to help researchers standardize medical image processing pipelines and allow them to focus on the deep learning experiments. It encourages good open-science practices, as it supports experiment reproducibility and is version-controlled so that the software can be cited precisely. Due to its modularity, the library is compatible with other frameworks for deep learning with medical images.}
}
}

@article{kostyszyn_intraprostatic_2020,
	title = {Intraprostatic Tumour Segmentation on {PSMA}-{PET} Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network},
	abstract = {Accurate delineation of the intraprostatic gross tumour volume ({GTV}) is a prerequisite for treatment approaches in patients with primary prostate cancer ({PCa}). Prostate-specific membrane antigen positron emission tomography ({PSMA}-{PET}) may outperform {MRI} in {GTV} detection. However, visual {GTV} delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural network ({CNN}) for automated segmentation of intraprostatic tumour ({GTV}-{CNN}) in {PSMA}-{PET}. Methods: The {CNN} (3D U-Net) was trained on 68Ga-{PSMA}-{PET} images of 152 patients from two different institutions and the training labels were generated manually using a validated technique. The {CNN} was tested on two independent internal (cohort 1: 68Ga-{PSMA}-{PET}, n = 18 and cohort 2: 18F-{PSMA}-{PET}, n = 19) and one external (cohort 3: 68Ga-{PSMA}-{PET}, n = 20) test-datasets. Accordance between manual contours and {GTV}-{CNN} was assessed with Dice-Sørensen coefficient ({DSC}). Sensitivity and specificity were calculated for the two internal test-datasets (cohort 1: n = 18, cohort 2: n = 11) by using whole-mount histology. Results: Median {DSCs} for cohorts 1-3 were 0.84 (range: 0.32-0.95), 0.81 (range: 0.28-0.93) and 0.83 (range: 0.32-0.93), respectively. Sensitivities and specificities for {GTV}-{CNN} were comparable with manual expert contours: 0.98 and 0.76 (cohort 1) and 1 and 0.57 (cohort 2), respectively. Computation time was around 6 seconds for a standard dataset. Conclusion: The application of a {CNN} for automated contouring of intraprostatic {GTV} in 68Ga-{PSMA}- and 18F-{PSMA}-{PET} images resulted in a high concordance with expert contours and in high sensitivities and specificities in comparison with histology reference. This robust, accurate and fast technique may be implemented for treatment concepts in primary {PCa}. The trained model and the study's source code are available in an open source repository.},
	journal = {Journal of Nuclear Medicine},
	author = {Kostyszyn, Dejan and Fechter, Tobias and Bartl, Nico and Grosu, Anca L. and Gratzke, Christian and Sigle, August and Mix, Michael and Ruf, Juri and Fassbender, Thomas F. and Kiefer, Selina and Bettermann, Alisa S. and Nicolay, Nils H. and Spohn, Simon and Kramer, Maria U. and Bronsert, Peter and Guo, Hongqian and Qiu, Xuefeng and Wang, Feng and Henkenberens, Christoph and Werner, Rudolf A. and Baltas, Dimos and Meyer, Philipp T. and Derlin, Thorsten and Chen, Mengxia and Zamboglou, Constantinos},
	date = {2020-10-30},
	pmid = {33127624},
	year = {2020},
	keywords = {Segmentation, Prostate cancer, {PET}, Histopathology, Convolutional neuronal networks, Genitourinary, Oncology: {GU}, {PSMA}-{PET}, Radiation Therapy Planning},
}

@online{1400-1700_iso_nodate,
	title = {{ISO} 12052:2017},
	shorttitle = {{ISO} 12052},
	abstract = {Health informatics — Digital imaging and communication in medicine ({DICOM}) including workflow and data management},
	titleaddon = {{ISO}},
	author = {{14:00-17:00}},
	langid = {english},
	file = {Snapshot:/home/anonymous/snap/zotero-snap/common/Zotero/storage/GXLCQL5J/72941.html:text/html},
}

@online{noauthor_teem_nodate,
	title = {Teem: nrrd: Definition of {NRRD} File Format},
	file = {Teem\: nrrd\: Definition of NRRD File Format:/home/anonymous/snap/zotero-snap/common/Zotero/storage/D7SNQ94X/format.html:text/html},
}

@article{joseph_effects_1980,
	title = {The effects of sampling on {CT} images},
	volume = {4},
	issn = {0363-8235},
	doi = {10.1016/0363-8235(80)90023-X},
	abstract = {Among the most important parameters that characterize {CT} data collection are the linear sample spacing, and the nominal beam-width. In this paper, we study the effects of these parameters on {CT} imaging using both a theoretical model and simulated images. An exact treatment of the point spread function in our model, which properly accounts for aliasing and reconstruction algorithm effects, is given for the case of parallel beam geometry. It is shown that it is always desirable to make the sample spacing as small as possible. In particular, aliasing artifacts are shown to decrease as sample spacing is decreased, both in the theoretical point spread function and in simulated {CT} images. Furthermore, abasing is shown to destroy the spatial invariance of the point spread function, whose shape will then depend on the exact location of the point in the scan field. Also investigated is the “volcano effect”, in which the point spread function develops a central depression when small sample spacing is used. It is shown that this effect produces no unusual distortions in the reconstruction of objects larger in diameter than the beam width; and further, that an attempt to avoid the volcano effect by means of inadequate sampling can have disastrous consequences for the reconstructed image.},
	pages = {189--206},
	number = {3},
	journaltitle = {Computerized Tomography},
	shortjournal = {Computerized Tomography},
	author = {Joseph, Peter M. and Spital, Robin D. and Stockham, Charles D.},
	date = {1980-01-01},
	langid = {english},
	keywords = {Computed tomography, Aliasing, Image reconstruction, Point spread function, Volcano effect},
	file = {ScienceDirect Full Text PDF:/home/anonymous/snap/zotero-snap/common/Zotero/storage/AAEWILE2/Joseph et al. - 1980 - The effects of sampling on CT images.pdf:application/pdf;ScienceDirect Snapshot:/home/anonymous/snap/zotero-snap/common/Zotero/storage/BJSGTVC7/036382358090023X.html:text/html},
}

@article{taha_metrics_2015,
	title = {Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool},
	volume = {15},
	abstract = {Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics.},
	pages = {29},
	number = {1},
	journal={BMC Medical Imaging},
	author = {Taha, Abdel Aziz and Hanbury, Allan},
	year = {2015},
	
}

@article{sudre_generalised_2017,
  title={Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations},
  author={Carole H. Sudre and Wenqi Li and Tom Kamiel Magda Vercauteren and S{\'e}bastien Ourselin and M. Jorge Cardoso},
  journal={Deep learning in medical image analysis and multimodal learning for clinical decision support},
  year={2017},
  pages={240-248}
}

@article{salembier_estro_2018,
	title = {{ESTRO} {ACROP} consensus guideline on {CT}- and {MRI}-based target volume delineation for primary radiation therapy of localized prostate cancer},
	volume = {127},
	issn = {0167-8140, 1879-0887},
	doi = {10.1016/j.radonc.2018.01.014},
	abstract = {{\textless}h2{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}h3{\textgreater}Background and purpose{\textless}/h3{\textgreater}{\textless}p{\textgreater}Delineation of clinical target volumes ({CTVs}) remains a weak link in radiation therapy ({RT}), and large inter-observer variation is seen. Guidelines for target and organs at risk delineation for prostate cancer in the primary setting are scarce. The aim was to develop a delineation guideline obtained by consensus between a broad European group of radiation oncologists.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Material and methods{\textless}/h3{\textgreater}{\textless}p{\textgreater}An {ESTRO} contouring consensus panel consisting of leading radiation oncologists and one radiologist with known subspecialty expertise in prostate cancer was asked to delineate the prostate, seminal vesicles and rectum on co-registered {CT} and {MRI} scans. After evaluation of the different contours, literature review and multiple informal discussions by electronic mail a {CTV} definition was defined and a guide for contouring the {CTV} of the prostate and the rectum was developed.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Results{\textless}/h3{\textgreater}{\textless}p{\textgreater}The panel achieved consensus {CTV} contouring definitions to be used as guideline for primary {RT} of localized prostate cancer.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Conclusion{\textless}/h3{\textgreater}{\textless}p{\textgreater}The {ESTRO} consensus on {CT}/{MRI} based {CTV} delineation for primary {RT} of localized prostate cancer, endorsed by a broad base of the radiation oncology community, is presented to improve consistency and reliability.{\textless}/p{\textgreater}},
	pages = {49--61},
	number = {1},
	journaltitle = {Radiotherapy and Oncology},
	shortjournal = {Radiotherapy and Oncology},
	author = {Salembier, Carl and Villeirs, Geert and Bari, Berardino De and Hoskin, Peter and Pieters, Bradley R. and Vulpen, Marco Van and Khoo, Vincent and Henry, Ann and Bossi, Alberto and Meerleer, Gert De and Fonteyne, Valérie},
	date = {2018-04-01},
	pmid = {29496279},
	note = {Publisher: Elsevier},
	file = {Volltext:/home/anonymous/snap/zotero-snap/common/Zotero/storage/YZNLST9E/Salembier et al. - 2018 - ESTRO ACROP consensus guideline on CT- and MRI-bas.pdf:application/pdf;Snapshot:/home/anonymous/snap/zotero-snap/common/Zotero/storage/8MMFFUZ7/fulltext.html:text/html},
}

@article{shahedi_semiautomatic_2018,
	title = {A semiautomatic segmentation method for prostate in {CT} images using local texture classification and statistical shape modeling},
	volume = {45},
	rights = {© 2018 American Association of Physicists in Medicine},
	issn = {2473-4209},
	doi = {https://doi.org/10.1002/mp.12898},
	abstract = {Purpose Prostate segmentation in computed tomography ({CT}) images is useful for treatment planning and procedure guidance such as external beam radiotherapy and brachytherapy. However, because of the low, soft tissue contrast of {CT} images, manual segmentation of the prostate is a time-consuming task with high interobserver variation. In this study, we proposed a semiautomated, three-dimensional (3D) segmentation for prostate {CT} images using shape and texture analysis and we evaluated the method against manual reference segmentations. Methods The prostate gland usually has a globular shape with a smoothly curved surface, and its shape could be accurately modeled or reconstructed having a limited number of well-distributed surface points. In a training dataset, using the prostate gland centroid point as the origin of a coordination system, we defined an intersubject correspondence between the prostate surface points based on the spherical coordinates. We applied this correspondence to generate a point distribution model for prostate shape using principal component analysis and to study the local texture difference between prostate and nonprostate tissue close to the different prostate surface subregions. We used the learned shape and texture characteristics of the prostate in {CT} images and then combined them with user inputs to segment a new image. We trained our segmentation algorithm using 23 {CT} images and tested the algorithm on two sets of 10 nonbrachytherapy and 37 postlow dose rate brachytherapy {CT} images. We used a set of error metrics to evaluate the segmentation results using two experts' manual reference segmentations. Results For both nonbrachytherapy and post-brachytherapy image sets, the average measured Dice similarity coefficient ({DSC}) was 88\% and the average mean absolute distance ({MAD}) was 1.9 mm. The average measured differences between the two experts on both datasets were 92\% ({DSC}) and 1.1 mm ({MAD}). Conclusions The proposed, semiautomatic segmentation algorithm showed a fast, robust, and accurate performance for 3D prostate segmentation of {CT} images, specifically when no previous, intrapatient information, that is, previously segmented images, was available. The accuracy of the algorithm is comparable to the best performance results reported in the literature and approaches the interexpert variability observed in manual segmentation.},
	pages = {2527--2541},
	number = {6},
	journaltitle = {Medical Physics},
	author = {Shahedi, Maysam and Halicek, Martin and Guo, Rongrong and Zhang, Guoyi and Schuster, David M. and Fei, Baowei},
	date = {2018},
	langid = {english},
	note = {\_eprint: https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.12898},
	keywords = {computer tomography ({CT}), prostate, segmentation, texture features},
	file = {Akzeptierte Version:/home/anonymous/snap/zotero-snap/common/Zotero/storage/XVGAVJPJ/Shahedi et al. - 2018 - A semiautomatic segmentation method for prostate i.pdf:application/pdf;Snapshot:/home/anonymous/snap/zotero-snap/common/Zotero/storage/TI55KWI2/mp.html:text/html},
}

@article{liu_automatic_2018,
	title = {Automatic segmentation of the prostate gland on planning ct images using deep neural networks ({DNN}).},
	volume = {45},
	pages = {e464},
	number = {6},
	journaltitle = {Med Phys},
	author = {Liu, Chang and Gardner, Stephen and Wen, Ning and Siddiqui, Farzan and Movsas, Benjamin and Chetty, Indrin},
	date = {2018-01-01},
	file = {"Automatic segmentation of the prostate gland on planning ct images usi" by Chang Liu, Stephen J. Gardner et al.:/home/anonymous/snap/zotero-snap/common/Zotero/storage/JI7IHQBF/112.html:text/html},
}

@article{lei_ct_2020,
	title = {{CT} prostate segmentation based on synthetic {MRI}-aided deep attention fully convolution network},
	volume = {47},
	rights = {© 2019 American Association of Physicists in Medicine},
	abstract = {Purpose Accurate segmentation of the prostate on computed tomography ({CT}) for treatment planning is challenging due to {CT}'s poor soft tissue contrast. Magnetic resonance imaging ({MRI}) has been used to aid prostate delineation, but its final accuracy is limited by {MRI}-{CT} registration errors. We developed a deep attention-based segmentation strategy on {CT}-based synthetic {MRI} ({sMRI}) to deal with the {CT} prostate delineation challenge without {MRI} acquisition. Methods and materials We developed a prostate segmentation strategy which employs an {sMRI}-aided deep attention network to accurately segment the prostate on {CT}. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an {sMRI} from {CT} images. Second, a deep attention fully convolution network was trained based on {sMRI} and the prostate contours deformed from {MRIs}. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's {CT} images into the trained {sMRI} generation model and segmentation model. Results The segmentation technique was validated with a clinical study of 49 patients by leave-one-out experiments and validated with an additional 50 patients by hold-out test. The Dice similarity coefficient, Hausdorff distance, and mean surface distance indices between our segmented and deformed {MRI}-defined prostate manual contours were 0.92 ± 0.09, 4.38 ± 4.66, and 0.62 ± 0.89 mm, respectively, with leave-one-out experiments, and were 0.91 ± 0.07, 4.57 ± 3.03, and 0.62 ± 0.65 mm, respectively, with hold-out test. Conclusions We have proposed a novel {CT}-only prostate segmentation strategy using {CT}-based {sMRI}, and validated its accuracy against the prostate contours that were manually drawn on {MRI} images and deformed to {CT} images. This technique could provide accurate prostate volume for treatment planning without requiring {MRI} acquisition, greatly facilitating the routine clinical workflow.},
	pages = {530--540},
	number = {2},
	journal = {Medical Physics},
	journaltitle = {Medical Physics},
	author = {Lei, Yang and Dong, Xue and Tian, Zhen and Liu, Yingzi and Tian, Sibo and Wang, Tonghe and Jiang, Xiaojun and Patel, Pretesh and Jani, Ashesh B. and Mao, Hui and Curran, Walter J. and Liu, Tian and Yang, Xiaofeng},
	year = {2020},
	langid = {english},
	keywords = {computed tomography, {CT}-based synthetic {MRI}, deep attention network, prostate segmentation},
	file = {Akzeptierte Version:/home/anonymous/snap/zotero-snap/common/Zotero/storage/2JZGERXX/Lei et al. - 2020 - CT prostate segmentation based on synthetic MRI-ai.pdf:application/pdf;Snapshot:/home/anonymous/snap/zotero-snap/common/Zotero/storage/QQ92SPXZ/mp.html:text/html},
}

@online{noauthor_fully_nodate,
	title = {Fully automated organ segmentation in male pelvic {CT} images - {IOPscience}},
	file = {Fully automated organ segmentation in male pelvic CT images - IOPscience:/home/anonymous/snap/zotero-snap/common/Zotero/storage/RNNPCUAP/aaf11c.html:text/html},
}

@article{rodieck_1965,
title = {Quantitative analysis of cat retinal ganglion cell response to visual stimuli},
journal = {Vision Research},
volume = {5},
number = {12},
pages = {583-601},
year = {1965},
author = {R.W. Rodieck},
}

@techreport{Petkov2005ModificationsOC,
title={Modifications of {C}enter-{S}urround, {S}pot {D}etection and {D}ot-{P}attern {S}elective {O}perators},
author={Nicolai Petkov and Wicher T. Visser},
year={2005},
number={2005-9-01},
institution={Institute of Mathematics and Computing Science, University of Groningen, Netherlands},
}

@article{kruizinga_petkov_2000, 
author={Kruizinga, Peter
and Petkov, Nicolai},
title={Computational model of dot-pattern selective cells},
journal={Biological Cybernetics},
year={2000},
month={9},
day={01},
volume={83},
number={4},
pages={313-325},
}



@article{pmlr-v139-babaiee21a,
  title = 	 {On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification},
  author =       {Babaiee, Zahra and Hasani, Ramin and Lechner, Mathias and Rus, Daniela and Grosu, Radu},
  journal = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {478--489},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/babaiee21a/babaiee21a.pdf},
}

@article{LITJENS2014359,
    title = {Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge},
    journal = {Medical Image Analysis},
    volume = {18},
    number = {2},
    pages = {359-373},
    year = {2014},
    author = {Geert Litjens and Robert Toth and Wendy (van de Ven) and Caroline Hoeks and Sjoerd Kerkstra and Bram (van Ginneken) and Graham Vincent and Gwenael Guillard and Neil Birbeck and Jindang Zhang and Robin Strand and Filip Malmberg and Yangming Ou and Christos Davatzikos and Matthias Kirschner and Florian Jung and Jing Yuan and Wu Qiu and Qinquan Gao and Philip Eddie Edwards and Bianca Maan and Ferdinand van-der Heijden and Soumya Ghose and Jhimli Mitra and Jason Dowling and Dean Barratt and Henkjan Huisman and Anant Madabhushi},
    keywords = {Segmentation, Prostate, MRI, Challenge},
    abstract = {Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.},
    url={https://promise12.grand-challenge.org}
}

@article{HASSABIS2017245,
title = {Neuroscience-Inspired Artificial Intelligence},
journal = {Neuron},
volume = {95},
number = {2},
pages = {245-258},
year = {2017},
author = {Demis Hassabis and Dharshan Kumaran and Christopher Summerfield and Matthew Botvinick},
keywords = {artificial intelligence, brain, cognition, neural network, learning},
abstract = {The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.}
}

@article{hassibis,
author = {Brooks, Rodney and Hassabis, Demis and Bray, Dennis and Shashua, Amnon},
year = {2012},
month = {02},
pages = {462-3},
title = {Turing centenary: Is the brain a good model for machine intelligence?},
volume = {482},
journal = {Nature},
}

@article{nayebiNeurips2018,
 author = {Nayebi, Aran and Bear, Daniel and Kubilius, Jonas and Kar, Kohitij and Ganguli, Surya and Sussillo, David and DiCarlo, James J and Yamins, Daniel L},
 journal = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 title = {Task-Driven Convolutional Recurrent Models of the Visual System},
 volume = {31},
 year = {2018}
 }

@article{nayebi2017biologically,
      title={Biologically inspired protection of deep networks from adversarial attacks}, 
      author={Aran Nayebi and Surya Ganguli},
      year={2017},
      eprint={1703.09202},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@article{li2019learning,
      title={Learning from brains how to regularize machines},
  author={Li, Zhe and Brendel, Wieland and Walker, Edgar and Cobos, Erick and Muhammad, Taliah and Reimer, Jacob and Bethge, Matthias and Sinz, Fabian and Pitkow, Zachary and Tolias, Andreas},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article {Dapello2020.06.16.154542_old,
	author = {Dapello, Joel and Marques, Tiago and Schrimpf, Martin and Geiger, Franziska and Cox, David D. and DiCarlo, James J.},
	title = {Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations},
	elocation-id = {2020.06.16.154542},
	year = {2020},
	doi = {10.1101/2020.06.16.154542},
	publisher = {Cold Spring Harbor Laboratory},
	eprint = {https://www.biorxiv.org/content/early/2020/06/17/2020.06.16.154542.full.pdf},
	journal = {bioRxiv}
}

@article{Dapello2020,
    author = {Dapello, Joel and Marques, Tiago and Schrimpf, Martin and Geiger, Franziska and Cox, David and DiCarlo, James J},
    journal = {Advances in Neural Information Processing Systems},
     editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
     pages = {13073--13087},
     title = {Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations},
     volume = {33},
     year = {2020}
}



@article{MA2021102035,
title = {Loss odyssey in medical image segmentation},
journal = {Medical Image Analysis},
volume = {71},
pages = {102035},
year = {2021},
author = {Jun Ma and Jianan Chen and Matthew Ng and Rui Huang and Yu Li and Chen Li and Xiaoping Yang and Anne L. Martel},
keywords = {Segmentation, Loss function, Convolutional neural networks, Benchmark},
abstract = {The loss function is an important component in deep learning-based segmentation methods. Over the past five years, many loss functions have been proposed for various segmentation tasks. However, a systematic study of the utility of these loss functions is missing. In this paper, we present a comprehensive review of segmentation loss functions in an organized manner. We also conduct the first large-scale analysis of 20 general loss functions on four typical 3D segmentation tasks involving six public datasets from 10+ medical centers. The results show that none of the losses can consistently achieve the best performance on the four segmentation tasks, but compound loss functions (e.g. Dice with TopK loss, focal loss, Hausdorff distance loss, and boundary loss) are the most robust losses. Our code and segmentation results are publicly available and can serve as a loss function benchmark. We hope this work will also provide insights on new loss function development for the community.}
}

@Article{thno61207,
author ="Spohn, Simon K.B. and Bettermann, Alisa S. and Bamberg, Fabian and Benndorf, Matthias and Mix, Michael and Nicolay, Nils H. and Fechter, Tobias and Hölscher, Tobias and Grosu, Radu and Chiti, Arturo and Grosu, Anca L. and Zamboglou, Constantinos",
title  ="Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies",
journal  ="Theranostics",
year  ="2021",
volume  ="11",
pages  ="8027-8042",
}

@misc{MDC_2019,
  author    = {Amber L. Simpson and
               Michela Antonelli and
               Spyridon Bakas and
               Michel Bilello and
               Keyvan Farahani and
               Bram van Ginneken and
               Annette Kopp{-}Schneider and
               Bennett A. Landman and
               Geert Litjens and
               Bjoern H. Menze and
               Olaf Ronneberger and
               Ronald M. Summers and
               Patrick Bilic and
               Patrick Ferdinand Christ and
               Richard K. G. Do and
               Marc Gollub and
               Jennifer Golia{-}Pernicka and
               Stephan Heckers and
               William R. Jarnagin and
               Maureen McHugo and
               Sandy Napel and
               Eugene Vorontsov and
               Lena Maier{-}Hein and
               M. Jorge Cardoso},
  title     = {A large annotated medical image dataset for the development and evaluation
               of segmentation algorithms},
  journal   = {arXiv preprint arXiv:1902.09063},
  year      = {2019},
  url={http://medicaldecathlon.com}
}

@misc{MDC_2021,
  author = {Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and Farahani, Keyvan and {AnnetteKopp-Schneider} and Landman, Bennett A. and Litjens, Geert and Menze, Bjoern and Ronneberger, Olaf and Summers, Ronald M. and van Ginneken, Bram and Bilello, Michel and Bilic, Patrick and Christ, Patrick F. and Do, Richard K. G. and Gollub, Marc J. and Heckers, Stephan H. and Huisman, Henkjan and Jarnagin, William R. and McHugo, Maureen K. and Napel, Sandy and Pernicka, Jennifer S. Goli and Rhode, Kawal and Tobon-Gomez, Catalina and Vorontsov, Eugene and Huisman, Henkjan and Meakin, James A. and Ourselin, Sebastien and Wiesenfarth, Manuel and Arbelaez, Pablo and Bae, Byeonguk and Chen, Sihong and Daza, Laura and Feng, Jianjiang and He, Baochun and Isensee, Fabian and Ji, Yuanfeng and Jia, Fucang and Kim, Namkug and Kim, Ildoo and Merhof, Dorit and Pai, Akshay and Park, Beomhee and Perslev, Mathias and Rezaiifar, Ramin and Rippel, Oliver and Sarasua, Ignacio and Shen, Wei and Son, Jaemin and Wachinger, Christian and Wang, Liansheng and Wang, Yan and Xia, Yingda and Xu, Daguang and Xu, Zhanwei and Zheng, Yefeng and Simpson, Amber L. and Maier-Hein, Lena and Cardoso, M. Jorge},
  
  keywords = {Image and Video Processing (eess.IV), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences, 68T07},
  
  title = {The Medical Segmentation Decathlon},
  
  year = {2021},
  journal={arXiv preprint arXiv:2106.05735},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

@article{tajbakhsh2020embracing,
  title={Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation},
  author={Tajbakhsh, Nima and Jeyaseelan, Laura and Li, Qian and Chiang, Jeffrey N and Wu, Zhihao and Ding, Xiaowei},
  journal={Medical Image Analysis},
  volume={63},
  pages={101693},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{tajbakhsh2020errornet,
  title={ErrorNet: learning error representations from limited data to improve vascular segmentation},
  author={Tajbakhsh, Nima and Lai, Brian and Ananth, Shilpa P and Ding, Xiaowei},
  booktitle={2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)},
  pages={1364--1368},
  year={2020},
  organization={IEEE}
}

@Article{MDC_2022,
author={Antonelli, Michela
and Reinke, Annika
and Bakas, Spyridon
and Farahani, Keyvan
and Kopp-Schneider, Annette
and Landman, Bennett A.
and Litjens, Geert
and Menze, Bjoern
and Ronneberger, Olaf
and Summers, Ronald M.
and van Ginneken, Bram
and Bilello, Michel
and Bilic, Patrick
and Christ, Patrick F.
and Do, Richard K. G.
and Gollub, Marc J.
and Heckers, Stephan H.
and Huisman, Henkjan
and Jarnagin, William R.
and McHugo, Maureen K.
and Napel, Sandy
and Pernicka, Jennifer S. Golia
and Rhode, Kawal
and Tobon-Gomez, Catalina
and Vorontsov, Eugene
and Meakin, James A.
and Ourselin, Sebastien
and Wiesenfarth, Manuel
and Arbel{\'a}ez, Pablo
and Bae, Byeonguk
and Chen, Sihong
and Daza, Laura
and Feng, Jianjiang
and He, Baochun
and Isensee, Fabian
and Ji, Yuanfeng
and Jia, Fucang
and Kim, Ildoo
and Maier-Hein, Klaus
and Merhof, Dorit
and Pai, Akshay
and Park, Beomhee
and Perslev, Mathias
and Rezaiifar, Ramin
and Rippel, Oliver
and Sarasua, Ignacio
and Shen, Wei
and Son, Jaemin
and Wachinger, Christian
and Wang, Liansheng
and Wang, Yan
and Xia, Yingda
and Xu, Daguang
and Xu, Zhanwei
and Zheng, Yefeng
and Simpson, Amber L.
and Maier-Hein, Lena
and Cardoso, M. Jorge},
title={The Medical Segmentation Decathlon},
journal={Nature Communications},
year={2022},
volume={13},
number={1},
pages={4128},
abstract={International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)---a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.},
}




@book{mack2013principles,
  title={Principles of Neural Science},
  author={Kandel, E.R. and Jessell, T.M. and Schwartz, J.H. and Siegelbaum, S.A. and Hudspeth, A.J.},
  lccn={2012023071},
  series={Fifth Edition},
  year={2013},
  publisher={McGraw-Hill Medical / Education}
}

@article{doi:10.1152/jn.1953.16.1.37,
author = {Kuffler, S.W.},
title = {Discharge {P}atterns and {F}unctional {O}rganization of {M}AMMALIAN {R}ETINA},
journal = {Journal of Neurophysiology},
volume = {16},
number = {1},
pages = {37-68},
year = {1953},
}

@article{enroth-cugell_pinto_1972,
title={Properties of the {S}urround {R}esponse {M}echanism of {C}at {R}etinal {G}anglion {C}ells and {C}entre-{S}urround {I}nteraction},
volume={220},
number={2},
journal={The Journal of Physiology},
author={Enroth-Cugell, C. and Pinto, L. H.},
year={1972},
month={Jan},
pages={403–439}}

@article{SHAPLEY1986229,
title = "Cat and monkey retinal ganglion cells and their visual functional roles",
journal = "Trends in Neurosciences",
volume = "9",
pages = "229 - 235",
year = "1986",
author = "R. Shapley and V.H. Perry",
abstract = "Retinal ganglion cells, the integrative-output neurons of the retina, can be sorted into functional classes. In the cat, two ganglion cell classes are labelled X and Y. These are distinguished by the different retinal subnetworks that provide their input. X cells are driven by a single linear receptive field center mechanism. Y cells receive center and surround signals and additional signals from nonlinear subunits in their receptive fields. Both X and Y cells are highly sensitive to contrast. X cells project almost exclusively to the A or A1 layers of the lateral geniculate nucleus (LGN). Y cell axons terminate in the A or A1 layers and also the more ventral C layers, and also the superior colliculus. In the monkey, P cells connect the retina to the parvocellular layers of the LGN, have small receptive fields, are wavelength-selective, and are insensitive to contrast. M cells are ganglion cells that send axons to the magnocellular layers of the LGN, are not wavelength-selective, have somewhat larger receptive fields than P cells, and are very sensitive to contrast. Comparisons between cat and monkey ganglion cell classes reveal several important similarities between M cells and X cells."
}

@article {Zaghloul2645,
	author = {Zaghloul, K.A. and Boahen, K. and Demb, J.B.},
	title = {Different Circuits for ON and OFF Retinal Ganglion Cells Cause Different Contrast Sensitivities},
	volume = {23},
	number = {7},
	pages = {2645--2654},
	year = {2003},
	publisher = {Society for Neuroscience},
	abstract = {The theory of {\textquotedblleft}parallel pathways{\textquotedblright} predicts that, except for a sign reversal, ON and OFF ganglion cells are driven by a similar presynaptic circuit. To test this hypothesis, we measured synaptic inputs to ON and OFF cells as reflected in the subthreshold membrane potential. We made intracellular recordings from brisk-transient (Y) cells in the in vitro guinea pig retina and show that ON and OFF cells in fact express significant asymmetries in their synaptic inputs. An ON cell receives relatively linear input that modulates a single excitatory conductance; whereas an OFF cell receives rectified input that modulates both inhibitory and excitatory conductances. The ON pathway, blocked by l-AP-4, tonically inhibits an OFF cell at mean luminance and phasically inhibits an OFF cell during a light increment. Our results suggest that basal glutamate release is high at ON but not OFF bipolar terminals, and inhibition between pathways is unidirectional: ON {\textrightarrow} OFF. These circuit asymmetries explain asymmetric contrast sensitivity observed in spiking behavior.},
	eprint = {https://www.jneurosci.org/content/23/7/2645.full.pdf},
	journal = {Journal of Neuroscience}
}

@article{doi:10.1113/jphysiol.2005.088047,
author = {Callaway, E.M.},
title = {Structure and {F}unction of {P}arallel {P}athways in the {P}rimate {E}arly {V}isual {S}ystem},
journal = {The Journal of Physiology},
volume = {566},
number = {1},
pages = {13-19},
eprint = {https://physoc.onlinelibrary.wiley.com/doi/pdf/10.1113/jphysiol.2005.088047},
abstract = {Parallel processing streams in the primate visual system originate from more than a dozen anatomically and functionally distinct types of retinal ganglion cells (RGCs). A central problem in determining how visual information is processed is understanding how each of these RGC types connects to more central structures, including the lateral geniculate nucleus (LGN) of the thalamus and (via the LGN) the primary visual cortex. Neverthelss, the available functional and anatomical evidence linking together specific cell types across these structures is surprisingly indirect. This review evaluates the available evidence and assesses the strength of the many inferences that can be made from these observations. There is strong evidence that parasol RGCs are the provenance of the magnocellular (M) visual pathway and that midget RGCs give rise to the parvocellular (P) pathway. Furthermore, the M and P pathways remain segregated up to the input layer of primary visual cortex. The relationships between the numerous other RGC types and cell types in the LGN remain less certain. and there remains ambiguity about how best to define additional pathways, such as the koniocellular (K) pathway, which probably arise from these other, less common, RGC types.},
year = {2005}
}

@article{3DConv_7410867,
  author={Tran, Du and Bourdev, Lubomir and Fergus, Rob and Torresani, Lorenzo and Paluri, Manohar},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)}, 
  title={Learning Spatiotemporal Features with 3D Convolutional Networks}, 
  year={2015},
  volume={},
  number={},
  pages={4489-4497},
  doi={10.1109/ICCV.2015.510}}
  

@article{gcn_2020,
author = {Tian, Zhiqiang and Li, Xiaojian and Zheng, Yaoyue and Chen, Zhang and Shi, Zhong and Liu, Lizhi and Fei, Baowei},
title = {Graph-convolutional-network-based interactive prostate segmentation in MR images},
journal = {Medical Physics},
volume = {47},
number = {9},
pages = {4164-4176},
abstract = {Purpose Accurate and robust segmentation of the prostate from magnetic resonance (MR) images is extensively applied in many clinical applications in prostate cancer diagnosis and treatment. The purpose of this study is the development of a robust interactive segmentation method for accurate segmentation of the prostate from MR images. Methods We propose an interactive segmentation method based on a graph convolutional network (GCN) to refine the automatically segmented results. An atrous multiscale convolutional neural network (CNN) encoder is proposed to learn representative features to obtain accurate segmentations. Based on the multiscale feature, a GCN block is presented to predict the prostate contour in both automatic and interactive manners. To preserve the prostate boundary details and effectively train the GCN, a contour matching loss is proposed. The performance of the proposed algorithm was evaluated on 41 in-house MR subjects and 30 PROMISE12 test subjects. Result The proposed method yields mean Dice similarity coefficients of 93.8 ± 1.2\% and 94.4 ± 1.0\% on our in-house and PROMISE12 datasets, respectively. The experimental results show that the proposed method outperforms several state-of-the-art segmentation methods. Conclusion The proposed interactive segmentation method based on the GCN can accurately segment the prostate from MR images. Our method has a variety of applications in prostate cancer imaging.},
year = {2020}
}


@article{gillespie2020deep,
    author = {Gillespie, David and Kendrick, Connah and Boon, Ian and Boon, Cheng and Rattay, Tim and Yap, Moi Hoon},
    title = {Deep learning in magnetic resonance prostate segmentation: A review and a new perspective},
    year = {2020},
    copyright = {Creative Commons Attribution 4.0 International},
    journal={arXiv preprint arXiv:2011.07795},
}

@article{segresnet2018,
    author="Myronenko, Andriy",
    editor="Crimi, Alessandro
    and Bakas, Spyridon
    and Kuijf, Hugo
    and Keyvan, Farahani
    and Reyes, Mauricio
    and van Walsum, Theo",
    title="3D MRI Brain Tumor Segmentation Using Autoencoder Regularization",
    journal="Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries",
    year="2019",
    pages="311-320",
    abstract="Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.",
    }
}

@misc{monai,
  author       = {MONAI Consortium},
  title        = {MONAI: Medical Open Network for AI},
  month        = dec,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.1.0},
  doi          = {10.5281/zenodo.7459814}}

@article{maier2022metrics,
  title={Metrics reloaded: Pitfalls and recommendations for image analysis validation},
  author={Maier-Hein, Lena and Reinke, Annika and Christodoulou, Evangelia and Glocker, Ben and Godau, Patrick and Isensee, Fabian and Kleesiek, Jens and Kozubek, Michal and Reyes, Mauricio and Riegler, Michael A and others},
  journal={arXiv preprint arXiv:2206.01653},
  year={2022}
}



@article{ALTINI202230,
title = {Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey},
journal = {Neurocomputing},
volume = {490},
pages = {30-53},
year = {2022},
author = {Nicola Altini and Berardino Prencipe and Giacomo Donato Cascarano and Antonio Brunetti and Gioacchino Brunetti and Vito Triggiani and Leonarda Carnimeo and Francescomaria Marino and Andrea Guerriero and Laura Villani and Arnaldo Scardapane and Vitoantonio Bevilacqua},
keywords = {Deep learning, Convolutional neural network, Conditional random fields, Self-supervised learning, Medical imaging, Computed tomography, Magnetic resonance imaging, Liver segmentation, Kidney segmentation, Spleen segmentation},
abstract = {Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing promising results, leading towards a revolution in the radiologists’ workflow. Precise delineations of abdominal organs boundaries reveal fundamental for a variety of purposes: surgical planning, volumetric estimation (e.g. Total Kidney Volume – TKV – assessment in Autosomal Dominant Polycystic Kidney Disease – ADPKD), diagnosis and monitoring of pathologies. Fundamental imaging techniques exploited for these tasks are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which enable clinicians to perform 3D analyses of all Regions of Interests (ROIs). In the realm of existing methods for segmentation and classification of these zones, Convolutional Neural Networks (CNNs) are emerging as the reference approach. In the last five years an enormous research effort has been done about the possibility of applying CNNs in Medical Imaging, resulting in more than 8000 documents on Scopus and more than 80000 results on Google Scholar. The high accuracy provided by those systems cannot be denied as motivation of all obtained results, though there are still problems to be addressed with. In this survey, major article databases, as Scopus, for instance, were systematically investigated for different kinds of Deep Learning approaches in segmentation of abdominal organs with a particular focus on liver, kidney and spleen. In this work, approaches are accurately classified, both by relevance of each organ (for instance, segmentation of liver has specific properties, if compared to other organs) and by type of computational approach, as well as the architecture of the employed network. For this purpose, a case study of segmentation for each of these organs is presented.}
}

@article{MOON2019109,
title = {Acceleration of spleen segmentation with end-to-end deep learning method and automated pipeline},
journal = {Computers in Biology and Medicine},
volume = {107},
pages = {109-117},
year = {2019},
author = {Hyeonsoo Moon and Yuankai Huo and Richard G. Abramson and Richard Alan Peters and Albert Assad and Tamara K. Moyo and Michael R. Savona and Bennett A. Landman},
keywords = {Clinical trial, Spleen segmentation, Deep learning, Docker, End-to-end automation, DICOM, Image processing},
abstract = {Delineation of Computed Tomography (CT) abdominal anatomical structure, specifically spleen segmentation, is useful for not only measuring tissue volume and biomarkers but also for monitoring interventions. Recently, segmentation algorithms using deep learning have been widely used to reduce time humans spend to label CT data. However, the computerized segmentation has two major difficulties: managing intermediate results (e.g., resampled scans, 2D sliced image for deep learning), and setting up the system environments and packages for autonomous execution. To overcome these issues, we propose an automated pipeline for the abdominal spleen segmentation. This pipeline provides an end-to-end synthesized process that allows users to avoid installing any packages and to deal with the intermediate results locally. The pipeline has three major stages: pre-processing of input data, segmentation of spleen using deep learning, 3D reconstruction with the generated labels by matching the segmentation results with the original image dimensions, which can then be used later and for display or demonstration. Given the same volume scan, the approach described here takes about 50 s on average whereas the manual segmentation takes about 30 min on the average. Even if it includes all subsidiary processes such as preprocessing and necessary setups, the whole pipeline process requires on the average 20 min from beginning to end.}
}

@article{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  journal={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}

@ARTICLE{Gibson2018,  
author={Gibson, Eli and Giganti, Francesco and Hu, Yipeng and Bonmati, Ester and Bandula, Steve and Gurusamy, Kurinchi and Davidson, Brian and Pereira, Stephen P. and Clarkson, Matthew J. and Barratt, Dean C.},  journal={IEEE Transactions on Medical Imaging},   title={Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks},   year={2018},  volume={37},  number={8},  pages={1822-1834},  
}

@article{hatamizadeh2022unetr,
  title={Unetr: Transformers for 3d medical image segmentation},
  author={Hatamizadeh, Ali and Tang, Yucheng and Nath, Vishwesh and Yang, Dong and Myronenko, Andriy and Landman, Bennett and Roth, Holger R and Xu, Daguang},
  journal={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={574--584},
  year={2022}
}

@inproceedings{nnunet_revisited_isensee,
  title={nnu-net revisited: A call for rigorous validation in 3d medical image segmentation},
  author={Isensee, Fabian and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus and Jaeger, Paul F},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={488--498},
  year={2024},
  organization={Springer}
}

@inproceedings{hatamizadeh2022swin,
  title={Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images},
  author={Hatamizadeh, Ali and Nath, Vishwesh and Tang, Yucheng and Yang, Dong and Roth, Holger R and Xu, Daguang},
  booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I},
  pages={272--284},
  year={2022},
  organization={Springer}
}

@article{chen2024transunet,
  title={TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers},
  author={Chen, Jieneng and Mei, Jieru and Li, Xianhang and Lu, Yongyi and Yu, Qihang and Wei, Qingyue and Luo, Xiangde and Xie, Yutong and Adeli, Ehsan and Wang, Yan and others},
  journal={Medical Image Analysis},
  pages={103280},
  year={2024},
  publisher={Elsevier}
}

@Article{Ilesanmi2021,
author={Ilesanmi, Ademola E.
and Ilesanmi, Taiwo O.},
title={Methods for image denoising using convolutional neural network: a review},
journal={Complex {\&} Intelligent Systems},
year={2021},
volume={7},
number={5},
pages={2179-2198},
abstract={Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.},
}

@misc{keno_bressem_2022_6481141,
  author       = {Keno Bressem and
                  Lisa Adams and
                  Günther Engel},
  title        = {Prostate158 - Training data},
  month        = apr,
  year         = 2022,
  publisher    = {Zenodo},
  version      = 1,
  doi          = {10.5281/zenodo.6481141},
  url          = {https://doi.org/10.5281/zenodo.6481141}
}

@ARTICLE{Zimmerer_2020,  
author={Zimmerer, David and Full, Peter M. and Isensee, Fabian and Jäger, Paul and Adler, Tim and Petersen, Jens and Köhler, Gregor and Ross, Tobias and Reinke, Annika and Kascenas, Antanas and Jensen, Bjørn Sand and O’Neil, Alison Q. and Tan, Jeremy and Hou, Benjamin and Batten, James and Qiu, Huaqi and Kainz, Bernhard and Shvetsova, Nina and Fedulova, Irina and Dylov, Dmitry V. and Yu, Baolun and Zhai, Jianyang and Hu, Jingtao and Si, Runxuan and Zhou, Sihang and Wang, Siqi and Li, Xinyang and Chen, Xuerun and Zhao, Yang and Marimont, Sergio Naval and Tarroni, Giacomo and Saase, Victor and Maier-Hein, Lena and Maier-Hein, Klaus},  journal={IEEE Transactions on Medical Imaging},   title={MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images},   year={2022},  volume={41},  number={10},  pages={2728-2738}}

@article{DURAN_2022,
title = {ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans},
journal = {Medical Image Analysis},
volume = {77},
pages = {102347},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102347},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521003923},
author = {Audrey Duran and Gaspard Dussert and Olivier Rouvière and Tristan Jaouen and Pierre-Marc Jodoin and Carole Lartizien},
keywords = {Semantic segmentation, Deep learning, Prostate cancer, Attention models, Computer-aided detection, Magnetic resonance imaging},
abstract = {Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent results in the detection of prostate cancer (PCa). However, characterizing prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical practice, and biopsy remains the reference to determine the Gleason score (GS). In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading. After encoding the information on a latent space, the network is separated in two branches: 1) the first branch performs prostate segmentation 2) the second branch uses this zonal prior as an attention gate for the detection and grading of prostate lesions. The model was trained and validated with a 5-fold cross-validation on a heterogeneous series of 219 MRI exams acquired on three different scanners prior prostatectomy. In the free-response receiver operating characteristics (FROC) analysis for clinically significant lesions (defined as GS >6) detection, our model achieves 69.0%±14.5% sensitivity at 2.9 false positive per patient on the whole prostate and  70.8%±14.4% sensitivity at 1.5 false positive when considering the peripheral zone (PZ) only. Regarding the automatic GS group grading, Cohen’s quadratic weighted kappa coefficient (κ) is 0.418±0.138, which is the best reported lesion-wise kappa for GS segmentation to our knowledge. The model has encouraging generalization capacities with κ=0.120±0.092 on the PROSTATEx-2 public dataset and achieves state-of-the-art performance for the segmentation of the whole prostate gland with a Dice of 0.875±0.013. Finally, we show that ProstAttention-Net improves performance in comparison to reference segmentation models, including U-Net, DeepLabv3+ and E-Net. The proposed attention mechanism is also shown to outperform Attention U-Net.}
}

@article{liu2023clip,
  title={Clip-driven universal model for organ segmentation and tumor detection},
  author={Liu, Jie and Zhang, Yixiao and Chen, Jie-Neng and Xiao, Junfei and Lu, Yongyi and Landman, Bennett A and Yuan, Yixuan and Yuille, Alan and Tang, Yucheng and Zhou, Zongwei},
  journal={arXiv preprint arXiv:2301.00785},
  year={2023}
}

@article{OpenAI2023GPT4TR,
  title={GPT-4 Technical Report},
  author={OpenAI},
  journal={ArXiv},
  year={2023},
  volume={abs/2303.08774}
}

@article{Bengio2021_turinglecture,
author = {Bengio, Yoshua and Lecun, Yann and Hinton, Geoffrey},
title = {Deep Learning for AI},
year = {2021},
issue_date = {July 2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {64},
number = {7},
abstract = {How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language?},
journal = {Commun. ACM},
month = {jun},
pages = {58–65},
numpages = {8}
}

@article{roy2023sam,
  title={Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model},
  author={Roy, Saikat and Wald, Tassilo and Koehler, Gregor and Rokuss, Maximilian R and Disch, Nico and Holzschuh, Julius and Zimmerer, David and Maier-Hein, Klaus H},
  journal={arXiv preprint arXiv:2304.05396},
  year={2023}
}

@article{kirillov2023_SAM_Orig,
  title={Segment anything},
  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
  journal={arXiv preprint arXiv:2304.02643},
  year={2023}
}

@article{ma2023sam,
  title={Segment anything in medical images},
  author={Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and You, Chenyu and Wang, Bo},
  journal={arXiv preprint arXiv:2304.12306},
  year={2023}
}

@article{mazurowski2023sam,
  title={Segment anything model for medical image analysis: an experimental study},
  author={Mazurowski, Maciej A and Dong, Haoyu and Gu, Hanxue and Yang, Jichen and Konz, Nicholas and Zhang, Yixin},
  journal={arXiv preprint arXiv:2304.10517},
  year={2023}
}

@article{deng2023sam,
  title={Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging},
  author={Deng, Ruining and Cui, Can and Liu, Quan and Yao, Tianyuan and Remedios, Lucas W and Bao, Shunxing and Landman, Bennett A and Wheless, Lee E and Coburn, Lori A and Wilson, Keith T and others},
  journal={arXiv preprint arXiv:2304.04155},
  year={2023}
}

@article{hu2023sam,
  title={When sam meets medical images: An investigation of segment anything model (sam) on multi-phase liver tumor segmentation},
  author={Hu, Chuanfei and Li, Xinde},
  journal={arXiv preprint arXiv:2304.08506},
  year={2023}
}

@article{sung2021global,
  title={Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries},
  author={Sung, Hyuna and Ferlay, Jacques and Siegel, Rebecca L and Laversanne, Mathieu and Soerjomataram, Isabelle and Jemal, Ahmedin and Bray, Freddie},
  journal={CA: a cancer journal for clinicians},
  volume={71},
  pages={209--249},
  year={2021},
  publisher={Wiley Online Library}
}

@Article{Zamboglou2019,
author={Zamboglou, Constantinos
and Fassbender, Thomas F.
and Steffan, Lina
and Schiller, Florian
and Fechter, Tobias
and Carles, Montserrat
and Kiefer, Selina
and Rischke, Hans C.
and Reichel, Kathrin
and Schmidt-Hegemann, Nina-Sophie
and Ilhan, Harun
and Chirindel, Alin F.
and Nicolas, Guillaume
and Henkenberens, Christoph
and Derlin, Thorsten
and Bronsert, Peter
and Mavroidis, Panayiotis
and Chen, Ronald C.
and Meyer, Philipp T.
and Ruf, Juri
and Grosu, Anca L.},
title={Validation of different PSMA-PET/CT-based contouring techniques for intraprostatic tumor definition using histopathology as standard of reference},
journal={Radiotherapy and Oncology},
year={2019},
month={Dec},
day={01},
publisher={Elsevier},
volume={141},
pages={208-213},
}



@article{kerkmeijer2021focal,
  title={Focal boost to the intraprostatic tumor in external beam radiotherapy for patients with localized prostate cancer: results from the FLAME randomized phase III trial},
  author={Kerkmeijer, Linda GW and Groen, Veerle H and Pos, Floris J and Haustermans, Karin and Monninkhof, Evelyn M and Smeenk, Robert Jan and Kunze-Busch, MC and den Boer, JC and Zijp, JVDVV and Vulpen, M van and others},
  year={2021},
  journal  = "Journal of Clinical Oncology",
  volume   =  39,
  number   =  7,
  pages    = "787--796",
  month    =  jan,
  language = "en"
}

@article{zamboglou2022psma,
  title={PSMA-PET-and MRI-based focal dose escalated radiation therapy of primary prostate cancer: Planned safety analysis of a nonrandomized 2-armed phase 2 trial (ARO2020-01)},
  author={Zamboglou, Constantinos and Spohn, Simon KB and Ruf, Juri and Benndorf, Matthias and Gainey, Mark and Kamps, Marius and Jilg, Cordula and Gratzke, Christian and Adebahr, Sonja and Schmidtmayer-Zamboglou, Barbara and others},
  journal={International Journal of Radiation Oncology* Biology* Physics},
  volume={113},
  number={5},
  pages={1025--1035},
  year={2022},
  publisher={Elsevier}
}

@Article{Holzschuh2023,
author={Holzschuh, Julius C.
and Mix, Michael
and Ruf, Juri
and H{\"o}lscher, Tobias
and Kotzerke, J{\"o}rg
and Vrachimis, Alexis
and Doolan, Paul
and Ilhan, Harun
and Marinescu, Ioana M.
and Spohn, Simon K.B.
and Fechter, Tobias
and Kuhn, Dejan
and Bronsert, Peter
and Gratzke, Christian
and Grosu, Radu
and Kamran, Sophia C.
and Heidari, Pedram
and Ng, Thomas S.C.
and K{\"o}nik, Arda
and Grosu, Anca-Ligia
and Zamboglou, Constantinos},
title={Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer},
journal={Radiotherapy and Oncology},
year={2023},
month={Nov},
day={01},
publisher={Elsevier},
volume={188},
}



@article{unet_pp_2018,
title={Unet++: Redesigning skip connections to exploit multiscale features in image segmentation},
author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming},
journal={IEEE transactions on medical imaging},
volume={39},
number={6},
pages={1856--1867},
year={2019},
publisher={IEEE}
}

@Article{Punn2022,
author={Punn, Narinder Singh
and Agarwal, Sonali},
title={Modality specific U-Net variants for biomedical image segmentation: a survey},
journal={Artificial Intelligence Review},
year={2022},
month={Mar},
day={01},
abstract={With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing (1) inter-modality, and (2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.},
}

@article{isensee2023look,
  title={Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans},
  author={Isensee, Fabian and Maier-Hein, Klaus H},
  journal={arXiv preprint arXiv:2309.13747},
  year={2023}
}

@article{bhandary2022ib,
  title={IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels},
  author={Bhandary, Shrajan and Babaiee, Zahra and Kostyszyn, Dejan and Fechter, Tobias and Zamboglou, Constantinos and Grosu, Anca-Ligia and Grosu, Radu},
  journal={arXiv preprint arXiv:2210.15949},
  year={2022}
}

@misc{Jeblick_TCIA_PSMA_PETCT_v1,
  author    = {Jeblick, K. and Schachtner, B. and Mittermeier, A. and Dexl, J. and Wesp, P. and K{\"u}stner, T. and Gatidis, S. and Fr{\"u}h, M. and Fabritius, M. and Unterrainer, L. and Sheikh, G. and Delker, A. and B{\"o}ning, G. and Brendel, M. and Ricke, J. and Werner, R. and Gu, S. and Ingrisch, M. and Geyer, T. and Cyran, C.},
  title     = {A whole-body {PSMA-PET/CT} dataset with manually annotated tumor lesions},
  note      = {Version 1 \textup{[Dataset]}},
  publisher = {The Cancer Imaging Archive},
  year      = {2024},
  doi       = {10.7937/R7EP-3X37},
  url       = {https://doi.org/10.7937/R7EP-3X37}
}

@misc{gatidis_2023_autopet,
  author       = {Sergios Gatidis and
                  Thomas Kustner and
                  Michael Ingrisch and
                  Clemens Cyran and
                  Jens Kleesiek},
  title        = {{Automated Lesion Segmentation in Whole-Body FDG- 
                   PET/CT - Domain Generalization}},
  month        = apr,
  year         = 2023,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.7845727},
  url          = {https://doi.org/10.5281/zenodo.7845727}
}

@article{TAMAL2020e05267,
title = {Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review},
journal = {Heliyon},
volume = {6},
number = {10},
pages = {e05267},
year = {2020},
author = {Mahbubunnabi Tamal},
keywords = {Biomedical engineering, Medical imaging, Oncology, Image processing, Computer-aided engineering, Intensity threshold, Segmentation, PET, Standardized uptake value (SUV)},
abstract = {Accurate, robust and reproducible delineation of tumour in Positron Emission Tomography (PET) is essential for diagnosis, treatment planning and response assessment. Since standardized uptake value (SUV) – a normalized semiquantitative parameter used in PET is represented by the intensity of the PET images and related to the radiotracer uptake, a SUV based threshold method is a natural choice to delineate the tumour. However, determination of an optimum threshold value is a challenging task due to low spatial resolution, and signal-to-noise ratio (SNR) along with finite image sampling constraint. The aim of the review is to summarize different fixed and adaptive threshold-based PET image segmentation approaches under a common mathematical framework Advantages and disadvantages of different threshold based methods are also highlighted from the perspectives of diagnosis, treatment planning and response assessment. Several fixed threshold values (30%–70% of the maximum SUV of the tumour (SUVmaxT)) have been investigated. It has been reported that the fixed threshold-based method is very much dependent on the SNR, tumour to background ratio (TBR) and the size of the tumour. Adaptive threshold-based method, an alternative to fixed threshold, can minimize these dependencies by accounting for tumour to background ratio (TBR) and tumour size. However, the parameters for the adaptive methods need to be calibrated for each PET camera system (e.g., scanner geometry, image acquisition protocol, reconstruction algorithm etc.) and it is not straight forward to implement the same procedure to other PET systems to obtain similar results. It has been reported that the performance of the adaptive methods is also not optimum for smaller volumes with lower TBR and SNR. Statistical analysis carried out on the NEMA thorax phantom images also indicates that regions segmented by the fixed threshold method are significantly different for all cases. On the other hand, the adaptive method provides significantly different segmented regions only for low TBR with different SNR. From this viewpoint, a robust threshold based segmentation method that will be less sensitive to SUVmaxT, SNR, TBR and volume needs to be developed. It was really challenging to compare the performance of different threshold-based methods because the performance of each method was tested on dissimilar data set with different data acquisition and reconstruction protocols along with different TBR, SNR and volumes. To avoid such difficulties, it will be desirable to have a common database of clinical PET images acquired with different image acquisition protocols and different PET cameras to compare the performance of automatic segmentation methods. It is also suggested to report the changes in SNR and TBR while reporting the response using threshold based methods.}
}

@article{spohn2020comparison,
  title={Comparison of manual and semi-automatic [18F] PSMA-1007 PET based contouring techniques for intraprostatic tumor delineation in patients with primary prostate cancer and validation with histopathology as standard of reference},
  author={Spohn, Simon KB and Kramer, Maria and Kiefer, Selina and Bronsert, Peter and Sigle, August and Schultze-Seemann, Wolfgang and Jilg, Cordula A and Sprave, Tanja and Ceci, Lara and Fassbender, Thomas F and others},
  journal={Frontiers in Oncology},
  volume={10},
  pages={600690},
  year={2020},
  publisher={Frontiers Media SA}
}

@article{eiber2016simultaneous,
  title={Simultaneous 68Ga-PSMA HBED-CC PET/MRI improves the localization of primary prostate cancer},
  author={Eiber, Matthias and Weirich, Gregor and Holzapfel, Konstantin and Souvatzoglou, Michael and Haller, Bernhard and Rauscher, Isabel and Beer, Ambros J and Wester, Hans-J{\"u}rgen and Gschwend, Juergen and Schwaiger, Markus and others},
  journal={European urology},
  volume={70},
  number={5},
  pages={829--836},
  year={2016},
  publisher={Elsevier}
}

@article{siegel_2024_CancerStatistics,
author = {Siegel, Rebecca L. and Giaquinto, Angela N. and Jemal, Ahmedin},
title = {Cancer statistics, 2024},
journal = {CA: A Cancer Journal for Clinicians},
volume = {74},
number = {1},
pages = {12-49},
keywords = {cancer cases, cancer statistics, death rates, incidence, mortality},
abstract = {Abstract Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes using incidence data collected by central cancer registries (through 2020) and mortality data collected by the National Center for Health Statistics (through 2021). In 2024, 2,001,140 new cancer cases and 611,720 cancer deaths are projected to occur in the United States. Cancer mortality continued to decline through 2021, averting over 4 million deaths since 1991 because of reductions in smoking, earlier detection for some cancers, and improved treatment options in both the adjuvant and metastatic settings. However, these gains are threatened by increasing incidence for 6 of the top 10 cancers. Incidence rates increased during 2015–2019 by 0.6\%–1\% annually for breast, pancreas, and uterine corpus cancers and by 2\%–3\% annually for prostate, liver (female), kidney, and human papillomavirus-associated oral cancers and for melanoma. Incidence rates also increased by 1\%–2\% annually for cervical (ages 30–44 years) and colorectal cancers (ages <55 years) in young adults. Colorectal cancer was the fourth-leading cause of cancer death in both men and women younger than 50 years in the late-1990s but is now first in men and second in women. Progress is also hampered by wide persistent cancer disparities; compared to White people, mortality rates are two-fold higher for prostate, stomach and uterine corpus cancers in Black people and for liver, stomach, and kidney cancers in Native American people. Continued national progress will require increased investment in cancer prevention and access to equitable treatment, especially among American Indian and Alaska Native and Black individuals.},
year = {2024}
}




@article{xu_automatic_2023,
	title = {Automatic segmentation of prostate cancer metastases in {PSMA} {PET}/{CT} images using deep neural networks with weighted batch-wise dice loss},
	volume = {158},
	language = {en},
	urldate = {2024-01-22},
	journal = {Computers in Biology and Medicine},
	author = {Xu, Yixi and Klyuzhin, Ivan and Harsini, Sara and Ortiz, Anthony and Zhang, Shun and Bénard, François and Dodhia, Rahul and Uribe, Carlos F. and Rahmim, Arman and Lavista Ferres, Juan},
	month = may,
	year = {2023},
	pages = {106882},
}


@article{draulans202168ga,
  title={68Ga-PSMA-11 PET, 18F-PSMA-1007 PET, and MRI for gross tumor volume delineation in primary prostate cancer: intermodality and intertracer variability},
  author={Draulans, C{\'e}dric and Pos, Floris and Smeenk, Robert J and Kerkmeijer, Linda and Vogel, Wouter V and Nagarajah, James and Janssen, Marcel and Mai, Cindy and Heijmink, Stijn and van der Leest, Marloes and others},
  journal={Practical Radiation Oncology},
  volume={11},
  number={3},
  pages={202--211},
  year={2021},
  publisher={Elsevier}
}

@article{hadlich2023autopet,
  title={AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net},
  author={Hadlich, Matthias and Marinov, Zdravko and Stiefelhagen, Rainer},
  journal={arXiv preprint arXiv:2309.12114},
  year={2023}
}

@article{murugesan2023autopet,
  title={Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge},
  author={Murugesan, Gowtham Krishnan and McCrumb, Diana and Brunner, Eric and Kumar, Jithendra and Soni, Rahul and Grigorash, Vasily and Moore, Stephen and Van Oss, Jeff},
  journal={arXiv preprint arXiv:2311.01574},
  year={2023}
}

@ARTICLE{slicer3dpaper,
  title     = "{3D} Slicer as an image computing platform for the Quantitative
               Imaging Network",
  author    = "Fedorov, Andriy and Beichel, Reinhard and Kalpathy-Cramer,
               Jayashree and Finet, Julien and Fillion-Robin, Jean-Christophe
               and Pujol, Sonia and Bauer, Christian and Jennings, Dominique
               and Fennessy, Fiona and Sonka, Milan and Buatti, John and
               Aylward, Stephen and Miller, James V and Pieper, Steve and
               Kikinis, Ron",
  journal   = "Magnetic Resonance Imaging",
  volume    =  30,
  number    =  9,
  pages     = "1323--1341",
  year      =  2012,
  language  = "en",
  url       = "http://www.slicer.org"
}


