@article{liu2021review,
  title={A review of deep-learning-based medical image segmentation methods},
  author={Liu, Xiangbin and Song, Liping and Liu, Shuai and Zhang, Yudong},
  journal={Sustainability},
  volume={13},
  number={3},
  pages={1224},
  year={2021},
  publisher={MDPI}
}

@article{simpson2019MSD,
  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
  author={Simpson, Amber L and Antonelli, Michela and Bakas, Spyridon and Bilello, Michel and Farahani, Keyvan and Van Ginneken, Bram and Kopp-Schneider, Annette and Landman, Bennett A and Litjens, Geert and Menze, Bjoern and others},
  journal={arXiv preprint arXiv:1902.09063},
  year={2019}
}

@misc{Yakubovskiy:2019,
  Author = {Pavel Yakubovskiy},
  Title = {Segmentation Models Pytorch},
  Year = {2020},
  Publisher = {GitHub},
  Journal = {GitHub repository},
  Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}}
}

@article{he_deep_2016,
	title = {Deep residual learning for image recognition},
	volume = {2016-Decem},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.90},
	abstract = {Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the {ImageNet} dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than {VGG} nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57\% error on the {ImageNet} test set. This result won the 1st place on the {ILSVRC} 2015 classification task. We also present analysis on {CIFAR}-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28\% relative improvement on the {COCO} object detection dataset. Deep residual nets are foundations of our submissions to {ILSVRC} \& {COCO} 2015 competitions1, where we also won the 1st places on the tasks of {ImageNet} detection, {ImageNet} localization, {COCO} detection, and {COCO} segmentation.},
	pages = {770--778},
	journaltitle = {Proceedings of the {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition},
	author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
	date = {2016},
	note = {{ISBN}: 9781467388504
\_eprint: 1512.03385},
	keywords = {{HasCode}},
	file = {Attachment:/Users/yhuang/OneDrive/papers/storage/3XGIX79G/He et al. - 2016 - Deep residual learning for image recognition.pdf:application/pdf},
}

@misc{ronneberger_u-net_2015,
	title = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
	url = {http://arxiv.org/abs/1505.04597},
	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.},
	publisher = {{arXiv}},
	author = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
	urldate = {2022-06-06},
	date = {2015-05-18},
	langid = {english},
	eprinttype = {arxiv},
	eprint = {1505.04597 [cs]},
	note = {Number: {arXiv}:1505.04597},
	keywords = {Medical Image, Semantic Segmentation},
	file = {Ronneberger 等。 - 2015 - U-Net Convolutional Networks for Biomedical Image.pdf:/Users/yhuang/OneDrive/papers/storage/KD2VT2G2/Ronneberger 等。 - 2015 - U-Net Convolutional Networks for Biomedical Image.pdf:application/pdf},
}

@article{KiTSDataset,
  title={An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging.},
  author={Heller, Nicholas and McSweeney, Sean and Peterson, Matthew Thomas and Peterson, Sarah and Rickman, Jack and Stai, Bethany and Tejpaul, Resha and Oestreich, Makinna and Blake, Paul and Rosenberg, Joel and others},
  volume = {38},
    number = {6},
    pages = {626-626},
    year = {2020},
  journal={American Society of Clinical Oncology}
}

@misc{NIHPancreas,
  title = {{NIH Pancreas}},
  howpublished = "\url{https://wiki.cancerimagingarchive.net/display/Public/Pancreas-CT}",
  year = {2020},
  note = "[Online; Accessed: Aug. 2020]"
}

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={234--241},
  year={2015}
}

@article{AbdomenCT-1K,
  title={AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?},
  author={Ma, Jun and Zhang, Yao and Gu, Song and Zhu, Cheng and Ge, Cheng and Zhang, Yichi and An, Xingle and Wang, Congcong and Wang, Qiyuan and Liu, Xin and Cao, Shucheng and Zhang, Qi and Liu, Shangqing and Wang, Yunpeng and Li, Yuhui and He, Jian and Yang, Xiaoping},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={10},
  pages={6695-6714},
  year={2022}
}


@article{isensee2021nnu,
  title={nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation},
  author={Isensee, Fabian and Jaeger, Paul F and Kohl, Simon AA and Petersen, Jens and Maier-Hein, Klaus H},
  journal={Nature Methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021}
}

@article{MedIA-FLARE21,
title = {Fast and Low-GPU-memory abdomen CT organ segmentation: The FLARE challenge},
author = {Jun Ma and Yao Zhang and Song Gu and Xingle An and Zhihe Wang and Cheng Ge and Congcong Wang and Fan Zhang and Yu Wang and Yinan Xu and Shuiping Gou and Franz Thaler and Christian Payer and Darko Štern and Edward G.A. Henderson and Dónal M. McSweeney and Andrew Green and Price Jackson and Lachlan McIntosh and Quoc-Cuong Nguyen and Abdul Qayyum and Pierre-Henri Conze and Ziyan Huang and Ziqi Zhou and Deng-Ping Fan and Huan Xiong and Guoqiang Dong and Qiongjie Zhu and Jian He and Xiaoping Yang},
journal = {Medical Image Analysis},
volume = {82},
pages = {102616},
year = {2022}
}

@article{LossOdyssey,
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}
}

@article{bilic2019lits,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick Ferdinand and Vorontsov, Eugene and Chlebus, Grzegorz and Chen, Hao and Dou, Qi and Fu, Chi-Wing and Han, Xiao and Heng, Pheng-Ann and Hesser, J{\"u}rgen and others},
  journal={arXiv preprint arXiv:1901.04056},
  year={2019}
}


@article{KiTS,
  title={The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge},
  author={Heller, Nicholas and Isensee, Fabian and Maier-Hein, Klaus H and Hou, Xiaoshuai and Xie, Chunmei and Li, Fengyi and Nan, Yang and Mu, Guangrui and Lin, Zhiyong and Han, Miofei and others},
  journal={Medical Image Analysis},
  volume={67},
  pages={101821},
  year={2021},
}
@misc{roth9data,
  title={Data from Pancreas-CT. The Cancer Imaging Archive (2016)},
  author={Roth, H and Farag, A and Turkbey, EB and Lu, L and Liu, J and Summers, RM}
}
@inproceedings{roth2015deeporgan,
  title={Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation},
  author={Roth, Holger R and Lu, Le and Farag, Amal and Shin, Hoo-Chang and Liu, Jiamin and Turkbey, Evrim B and Summers, Ronald M},
  booktitle={International conference on medical image computing and computer-assisted intervention},
  pages={556--564},
  year={2015},
  organization={Springer}
}
@article{clark2013TCIA,
  title={The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  author={Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David and Pringle, Michael and others},
  journal={Journal of Digital Imaging},
  volume={26},
  number={6},
  pages={1045--1057},
  year={2013}
}

@article{cps,
	title = {Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision},
	issn = {10636919},
	doi = {10.1109/CVPR46437.2021.00264},
	abstract = {In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization approach, called cross pseudo supervision ({CPS}). Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image. The pseudo one-hot label map, output from one perturbed segmentation network, is used to supervise the other segmentation network with the standard cross-entropy loss, and vice versa. The {CPS} consistency has two roles: encourage high similarity between the predictions of two perturbed networks for the same input image, and expand training data by using the unlabeled data with pseudo labels. Experiment results show that our approach achieves the state-of-the-art semi-supervised segmentation performance on Cityscapes and {PASCAL} {VOC} 2012.},
	pages = {2613--2622},
	journaltitle = {Proceedings of the {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition},
	author = {Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong},
	date = {2021},
	note = {{ISBN}: 9781665445092
\_eprint: 2106.01226},
	keywords = {Semantic Segmentation, Semi-supervised, {HasCode}},
	file = {Attachment:D\:\\OneDrive\\papers\\storage\\TE4BGIB4\\Chen et al. - 2021 - Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision.pdf:application/pdf},
}

@article{fang2020multi,
  title={Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction},
  author={Fang, Xi and Yan, Pingkun},
  journal={IEEE Transactions on Medical Imaging},
  volume={39},
  number={11},
  pages={3619--3629},
  year={2020},
  publisher={IEEE}
}

@article{monteiro2020multiclass,
  title={Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study},
  author={Monteiro, Miguel and Newcombe, Virginia FJ and Mathieu, Francois and Adatia, Krishma and Kamnitsas, Konstantinos and Ferrante, Enzo and Das, Tilak and Whitehouse, Daniel and Rueckert, Daniel and Menon, David K and others},
  journal={The Lancet Digital Health},
  volume={2},
  number={6},
  pages={e314--e322},
  year={2020},
  publisher={Elsevier}
}



@article{shen2017deep,
  title={Deep learning in medical image analysis},
  author={Shen, Dinggang and Wu, Guorong and Suk, Heung-Il},
  journal={Annual review of biomedical engineering},
  volume={19},
  pages={221},
  year={2017},
  publisher={NIH Public Access}
}

@article{li2020transformation,
  title={Transformation-consistent self-ensembling model for semisupervised medical image segmentation},
  author={Li, Xiaomeng and Yu, Lequan and Chen, Hao and Fu, Chi-Wing and Xing, Lei and Heng, Pheng-Ann},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={32},
  number={2},
  pages={523--534},
  year={2020},
  publisher={IEEE}
}

@inproceedings{yu2019uncertainty,
  title={Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation},
  author={Yu, Lequan and Wang, Shujun and Li, Xiaomeng and Fu, Chi-Wing and Heng, Pheng-Ann},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={605--613},
  year={2019},
  organization={Springer}
}

@inproceedings{li2020shape,
  title={Shape-aware semi-supervised 3D semantic segmentation for medical images},
  author={Li, Shuailin and Zhang, Chuyu and He, Xuming},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={552--561},
  year={2020},
  organization={Springer}
}

@inproceedings{luo2021semi,
  title={Semi-supervised medical image segmentation through dual-task consistency},
  author={Luo, Xiangde and Chen, Jieneng and Song, Tao and Wang, Guotai},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={10},
  pages={8801--8809},
  year={2021}
}

@article{isensee2018nnu,
  title={nnu-net: Self-adapting framework for u-net-based medical image segmentation},
  author={Isensee, Fabian and Petersen, Jens and Klein, Andre and Zimmerer, David and Jaeger, Paul F and Kohl, Simon and Wasserthal, Jakob and Koehler, Gregor and Norajitra, Tobias and Wirkert, Sebastian and others},
  journal={arXiv preprint arXiv:1809.10486},
  year={2018}
}

@article{du2020medical,
  title={Medical image segmentation based on u-net: A review},
  author={Du, Getao and Cao, Xu and Liang, Jimin and Chen, Xueli and Zhan, Yonghua},
  journal={Journal of Imaging Science and Technology},
  volume={64},
  pages={1--12},
  year={2020},
  publisher={Society for Imaging Science and Technology}
}

