@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 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 others},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume={44},
  number={10},
  pages={6695-6714},
  year={2022}
}

@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 others},
journal = {Medical Image Analysis},
volume = {82},
pages = {102616},
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 others},
  journal={Nature Methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021}
}

@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 others}
}

@article{bilic2019lits,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick Ferdinand and Vorontsov, Eugene 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 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 others}
}
@inproceedings{roth2015deeporgan,
  title={Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation},
  author={Roth, Holger R and Lu, Le and Farag, Amal and others},
  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 others},
  journal={Journal of Digital Imaging},
  volume={26},
  number={6},
  pages={1045--1057},
  year={2013}
}

@article{siegel2022cancer,
  title={Cancer statistics, 2022},
  author={Siegel, Rebecca L and Miller, Kimberly D and Fuchs, Hannah E and others},
  journal={Ca Cancer J Clin},
  volume={72},
  number={1},
  pages={7--33},
  year={2022}
}

@article{bilic2023liver,
  title={The liver tumor segmentation benchmark (lits)},
  author={Bilic, Patrick and Christ, Patrick and Li, Hongwei Bran and others},
  journal={Medical Image Analysis},
  volume={84},
  pages={102680},
  year={2023},
  publisher={Elsevier}
}

@article{heller2021state,
  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 others},
  journal={Medical image analysis},
  volume={67},
  pages={101821},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{zhou2019semi,
  title={Semi-supervised 3D abdominal multi-organ segmentation via deep multi-planar co-training},
  author={Zhou, Yuyin and Wang, Yan and Tang, Peng and others},
  booktitle={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  pages={121--140},
  year={2019},
  organization={IEEE}
}

@inproceedings{lee2020semi,
  title={Semi-supervised multi-organ segmentation through quality assurance supervision},
  author={Lee, Ho Hin and Tang, Yucheng and Tang, Olivia and others},
  booktitle={Medical Imaging 2020: Image Processing},
  volume={11313},
  pages={363--369},
  year={2020},
  organization={SPIE}
}

@incollection{huang2022revisiting,
  title={Revisiting nnU-net for iterative pseudo labeling and efficient sliding window inference},
  author={Huang, Ziyan and Wang, Haoyu and Ye, Jin and others},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={178--189},
  year={2022},
  publisher={Springer}
}

@incollection{alves2022uncertainty,
  title={Uncertainty-guided self-learning framework for semi-supervised multi-organ segmentation},
  author={Alves, Nat{\'a}lia and de Wilde, Bram},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={116--127},
  year={2022},
  publisher={Springer}
}

@incollection{gao2022pseudo,
  title={A pseudo-labeling approach to semi-supervised organ segmentation},
  author={Gao, Jianwei and Xu, Juan and Fei, Honggao},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={318--326},
  year={2022},
  publisher={Springer}
}

@incollection{han2022efficient,
  title={Efficient semi-supervised multi-organ segmentation using uncertainty rectified pyramid consistency},
  author={Han, Meng and Qu, Yijie and Luo, Xiangde},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={307--317},
  year={2022},
  publisher={Springer}
}

@incollection{lai2022dlunet,
  title={DLUNet: semi-supervised learning based dual-light UNet for multi-organ segmentation},
  author={Lai, Haoran and Wang, Tao and Zhou, Shuoling},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={64--73},
  year={2022},
  publisher={Springer}
}

@incollection{meng2022uncertainty,
  title={Uncertainty-aware mean teacher framework with inception and squeeze-and-excitation block for MICCAI FLARE22 challenge},
  author={Meng, Hui and Zhao, Haochen and Yu, Ziniu and others},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={245--259},
  year={2022},
  publisher={Springer}
}

@incollection{choi2022knowledge,
  title={Knowledge distillation from cross teaching teachers for efficient semi-supervised abdominal organ segmentation in CT},
  author={Choi, Jae Won},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={101--115},
  year={2022},
  publisher={Springer}
}

@incollection{huang20223d,
  title={3D cross-pseudo supervision (3D-CPS): A semi-supervised nnU-Net architecture for abdominal organ segmentation},
  author={Huang, Yongzhi and Zhang, Hanwen and Yan, Yan and others},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={87--100},
  year={2022},
  publisher={Springer}
}

@article{eisenhauer2009new,
  title={New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)},
  author={Eisenhauer, Elizabeth A and Therasse, Patrick and Bogaerts, Jan and others},
  journal={European Journal of Cancer},
  volume={45},
  number={2},
  pages={228--247},
  year={2009},
  publisher={Elsevier}
}

@article{simpson2019MSD,
author = {Amber L. Simpson, Michela Antonelli, Spyridon Bakas and others},
title = {A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
year = {2019},
journal = {arXiv preprint arXiv:1902.09063},
}


@inproceedings{U-Net,
  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}
}

@inproceedings{FLARE22-1st-Huang,
  title={Revisiting nnU-net for iterative pseudo labeling and efficient sliding window inference},
  author={Huang, Ziyan and Wang, Haoyu and Ye, Jin and others},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={178--189},
  year={2022},
  publisher={Springer}
}

@inproceedings{FLARE22-bestDSC-Wang,
  title={Cascade dual-decoders network for abdominal organs segmentation},
  author={Wang, Ershuai and Zhao, Yaliang and Wu, Yajun},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={202--213},
  year={2022},
  publisher={Springer},
}

@article{FLARE22,
    author = {Jun Ma and Yao Zhang and Song Gu and others},
    title = {Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the FLARE22 Challenge},
    year = {2023},
    journal = {arXiv preprint arXiv:2308.05862},
}

@article{MedSAM,
  title={Segment anything in medical images},
  author={Ma, Jun and Wang, Bo},
  journal={arXiv preprint arXiv:2304.12306},
  year={2023}
}

@article{nnUNet,
  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 others},
  journal={Nature Methods},
  volume={18},
  number={2},
  pages={203--211},
  year={2021}
}

@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 others}
}


@article{LiTS,
    title = {The Liver Tumor Segmentation Benchmark (LiTS)},
    journal = {Medical Image Analysis},
    volume = {84},
    pages = {102680},
    year = {2023},
    author = {Patrick Bilic and Patrick Christ and Hongwei Bran Li and others},
}


@article{TotalSegmentator,
author = {Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T. and others},
title = {TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images},
journal = {Radiology: Artificial Intelligence},
volume = {5},
number = {5},
pages = {e230024},
year = {2023}
}

@article{autoPET-Data,
  title={A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions},
  author={Gatidis, Sergios and Hepp, Tobias and Fr{\"u}h, Marcel and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={601},
  year={2022}
}

@article{autoPET-MICCAI22,
  title={The autoPET challenge: Towards fully automated lesion segmentation in oncologic PET/CT imaging},
  author={Gatidis, Sergios and Fr{\"u}h, Marcel and Fabritius, Matthias and others},
  journal={preprint at Research Square (Nature Portfolio )},
  doi={https://doi.org/10.21203/rs.3.rs-2572595/v1},
  year={2023}
}


@article{TCIA,
  title={The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository},
  author={Clark, Ken and Vendt, Bruce and Smith, Kirk and others},
  journal={Journal of Digital Imaging},
  volume={26},
  number={6},
  pages={1045--1057},
  year={2013}
}

@inproceedings{ITKSNAP,
  title={ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images},
  author={Yushkevich, Paul A and Gao, Yang and Gerig, Guido},
  booktitle={Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  pages={3342--3345},
  year={2016}
}


@article{codalab,
  author  = {Adrien Pavao and Isabelle Guyon and Anne-Catherine Letournel and others},
  title   = {CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges},
  journal = {Journal of Machine Learning Research},
  year    = {2023},
  volume  = {24},
  number  = {198},
  pages   = {1--6}
}