@article{zhu2023embedding,
  title={Embedding expertise knowledge into inverse treatment planning for low-dose-rate brachytherapy of hepatic malignancies},
  author={Zhu, Jianjun and Wang, Cheng and Teng, Sitong and Lu, Jian and Lyu, Pengju and Zhang, Pujun and Xu, Jun and Lu, Ligong and Teng, Gao-Jun},
  journal={Medical Physics},
  year={2023},
  publisher={Wiley Online Library}
}
@inproceedings{frigo1998fftw,
  title={FFTW: An adaptive software architecture for the FFT},
  author={Frigo, Matteo and Johnson, Steven G},
  booktitle={Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP'98 (Cat. No. 98CH36181)},
  volume={3},
  pages={1381--1384},
  year={1998},
  organization={IEEE}
}

@inproceedings{xie2021cotr,
  title={Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation},
  author={Xie, Yutong and Zhang, Jianpeng and Shen, Chunhua and Xia, Yong},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part III 24},
  pages={171--180},
  year={2021},
  organization={Springer}
}

@inproceedings{jiang2022apaunet,
  title={APAUNet: Axis Projection Attention UNet for Small Target in 3D Medical Segmentation},
  author={Jiang, Yuncheng and Zhang, Zixun and Qin, Shixi and Guo, Yao and Li, Zhen and Cui, Shuguang},
  booktitle={Proceedings of the Asian Conference on Computer Vision},
  pages={283--298},
  year={2022}
}
@article{zhang2021weakly,
  title={Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images},
  author={Zhang, Dong and Chen, Bo and Chong, Jaron and Li, Shuo},
  journal={Medical Image Analysis},
  volume={70},
  pages={102005},
  year={2021},
  publisher={Elsevier}
}
@article{li2017comprehensive,
  title={A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data},
  author={Li, Yuanyuan and Kang, Kai and Krahn, Juno M and Croutwater, Nicole and Lee, Kevin and Umbach, David M and Li, Leping},
  journal={BMC genomics},
  volume={18},
  pages={1--13},
  year={2017},
  publisher={Springer}
}

@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{V-net,
  title={V-net: Fully convolutional neural networks for volumetric medical image segmentation},
  author={Milletari, Fausto and Navab, Nassir and Ahmadi, Seyed-Ahmad},
  booktitle={2016 fourth international conference on 3D vision (3DV)},
  pages={565--571},
  year={2016},
  organization={Ieee}
}

@inproceedings{Unet3,
  title={Unet 3+: A full-scale connected unet for medical image segmentation},
  author={Huang, Huimin and Lin, Lanfen and Tong, Ruofeng and Hu, Hongjie and Zhang, Qiaowei and Iwamoto, Yutaro and Han, Xianhua and Chen, Yen-Wei and Wu, Jian},
  booktitle={ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP)},
  pages={1055--1059},
  year={2020},
  organization={IEEE}
}

@inproceedings{Unetr,
  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},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={574--584},
  year={2022}
}

@article{zhou2021nnformer,
  title={nnformer: Interleaved transformer for volumetric segmentation},
  author={Zhou, Hong-Yu and Guo, Jiansen and Zhang, Yinghao and Yu, Lequan and Wang, Liansheng and Yu, Yizhou},
  journal={arXiv preprint arXiv:2109.03201},
  year={2021}
}


@inproceedings{gao2021utnet,
  title={UTNet: a hybrid transformer architecture for medical image segmentation},
  author={Gao, Yunhe and Zhou, Mu and Metaxas, Dimitris N},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part III 24},
  pages={61--71},
  year={2021},
  organization={Springer}
}
@article{li2201transbtsv2,
  title={TransBTSV2: Wider Instead of Deeper Transformer for Medical Image Segmentation. arXiv 2022},
  author={Li, J and Wang, W and Chen, C and Zhang, T and Zha, S and Yu, H and Wang, J},
  journal={arXiv preprint arXiv:2201.12785}
}
@article{valanarasu2203unext,
  title={UNeXt: MLP-based Rapid Medical Image Segmentation Network. arXiv 2022},
  author={Valanarasu, JMJ and Patel, VM},
  journal={arXiv preprint arXiv:2203.04967}
}
@article{liu2023towards,
  title={Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation},
  author={Liu, Xiaoyu and Qu, Linhao and Xie, Ziyue and Zhao, Jiayue and Shi, Yonghong and Song, Zhijian},
  journal={arXiv preprint arXiv:2303.00232},
  year={2023}
}
@article{lian2023learning,
  title={Learning multi-organ segmentation via partial-and mutual-prior from single-organ datasets},
  author={Lian, Sheng and Li, Lei and Luo, Zhiming and Zhong, Zhun and Wang, Beizhan and Li, Shaozi},
  journal={Biomedical Signal Processing and Control},
  volume={80},
  pages={104339},
  year={2023},
  publisher={Elsevier}
}

@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 Niu, Jingqi and Tu, Can and Yang, Yuncheng and Du, Shiyi and Deng, Zhongying and Gu, Lixu and He, Junjun},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={178--189},
  year={2022},
  publisher={Springer}
}

@article{li2023multi,
  title={Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation},
  author={Li, Shiman and Wang, Haoran and Meng, Yucong and Zhang, Chenxi and Song, Zhijian},
  journal={arXiv preprint arXiv:2302.03296},
  year={2023}
}
@article{tarvainen2017mean,
  title={Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results},
  author={Tarvainen, Antti and Valpola, Harri},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@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{pan2022unlabeled,
  title={Unlabeled Abdominal Multi-organ Image Segmentation Based on Semi-supervised Adversarial Training Strategy},
  author={Pan, YuanKe and Zhu, Jinxin and Huang, Bingding},
  booktitle={MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation},
  pages={11--22},
  year={2022},
  publisher={Springer}
}

@inproceedings{zhang2017deep,
  title={Deep adversarial networks for biomedical image segmentation utilizing unannotated images},
  author={Zhang, Yizhe and Yang, Lin and Chen, Jianxu and Fredericksen, Maridel and Hughes, David P and Chen, Danny Z},
  booktitle={Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20},
  pages={408--416},
  year={2017},
  organization={Springer}
}

@article{wang2019organ,
  title={Organ at risk segmentation in head and neck CT images using a two-stage segmentation framework based on 3D U-Net},
  author={Wang, Yueyue and Zhao, Liang and Wang, Manning and Song, Zhijian},
  journal={IEEE Access},
  volume={7},
  pages={144591--144602},
  year={2019},
  publisher={IEEE}
}
@article{wangcascade,
  title={Cascade dual-decoders Network for Abdominal Organs},
  author={Wang, Ershuai and Zhao, Yaliang and Wu, Yajun}
}

@inproceedings{chen2023run,
  title={Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks},
  author={Chen, Jierun and Kao, Shiu-hong and He, Hao and Zhuo, Weipeng and Wen, Song and Lee, Chul-Ho and Chan, S-H Gary},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12021--12031},
  year={2023}
}

@article{xie2021segformer,
  title={SegFormer: Simple and efficient design for semantic segmentation with transformers},
  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={12077--12090},
  year={2021}
}

@inproceedings{yu2022metaformer,
  title={Metaformer is actually what you need for vision},
  author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={10819--10829},
  year={2022}
}

@inproceedings{ren2022shunted,
  title={Shunted self-attention via multi-scale token aggregation},
  author={Ren, Sucheng and Zhou, Daquan and He, Shengfeng and Feng, Jiashi and Wang, Xinchao},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10853--10862},
  year={2022}
}

@article{lin2023scale,
  title={Scale-Aware Modulation Meet Transformer},
  author={Lin, Weifeng and Wu, Ziheng and Chen, Jiayu and Huang, Jun and Jin, Lianwen},
  journal={arXiv preprint arXiv:2307.08579},
  year={2023}
}



@inproceedings{tang2022self,
  title={Self-supervised pre-training of swin transformers for 3d medical image analysis},
  author={Tang, Yucheng and Yang, Dong and Li, Wenqi and Roth, Holger R and Landman, Bennett and Xu, Daguang and Nath, Vishwesh and Hatamizadeh, Ali},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20730--20740},
  year={2022}
}

@inproceedings{hu2023label,
  title={Label-free liver tumor segmentation},
  author={Hu, Qixin and Chen, Yixiong and Xiao, Junfei and Sun, Shuwen and Chen, Jieneng and Yuille, Alan L and Zhou, Zongwei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7422--7432},
  year={2023}
}


@article{simpson2019MSD,
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 Menze and Olaf Ronneberger and Ronald M. Summers and Patrick Bilic and Patrick F. Christ and Richard K. G. Do and Marc Gollub and Jennifer Golia-Pernicka and Stephan H. Heckers and William R. Jarnagin and Maureen K. 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},
year = {2019},
journal = {arXiv preprint arXiv:1902.09063},
}

@article{KiTS,
title = {The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge},
journal = {Medical Image Analysis},
volume = {67},
pages = {101821},
year = {2021},
author = {Nicholas Heller and Fabian Isensee and Klaus H. Maier-Hein and Xiaoshuai Hou and Chunmei Xie and Fengyi Li and Yang Nan and Guangrui Mu and Zhiyong Lin and Miofei Han and Guang Yao and Yaozong Gao and Yao Zhang and Yixin Wang and Feng Hou and Jiawei Yang and Guangwei Xiong and Jiang Tian and Cheng Zhong and Jun Ma and Jack Rickman and Joshua Dean and Bethany Stai and Resha Tejpaul and Makinna Oestreich and Paul Blake and Heather Kaluzniak and Shaneabbas Raza and Joel Rosenberg and Keenan Moore and Edward Walczak and Zachary Rengel and Zach Edgerton and Ranveer Vasdev and Matthew Peterson and Sean McSweeney and Sarah Peterson and Arveen Kalapara and Niranjan Sathianathen and Nikolaos Papanikolopoulos and Christopher Weight}
}


@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}
}





@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{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}
}



@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 Cheng Ge and Shihao Ma and Adamo Young and Cheng Zhu and Kangkang Meng and Xin Yang and Ziyan Huang and Fan Zhang and Wentao Liu and YuanKe Pan and Shoujin Huang and Jiacheng Wang and Mingze Sun and Weixin Xu and Dengqiang Jia and Jae Won Choi and Natália Alves and Bram de Wilde and Gregor Koehler and Yajun Wu and Manuel Wiesenfarth and Qiongjie Zhu and Guoqiang Dong and Jian He and the FLARE Challenge Consortium and Bo Wang},
    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 Petersen, Jens and Maier-Hein, Klaus H},
  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 Rui Huang and Yu Li and Chen Li and Xiaoping Yang and Anne L. Martel}
}


@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 Eugene Vorontsov and Avi Ben-Cohen and Georgios Kaissis and Adi Szeskin and Colin Jacobs and Gabriel Efrain Humpire Mamani and Gabriel Chartrand and Fabian Lohöfer and Julian Walter Holch and Wieland Sommer and Felix Hofmann and Alexandre Hostettler and Naama Lev-Cohain and Michal Drozdzal and Michal Marianne Amitai and Refael Vivanti and Jacob Sosna and Ivan Ezhov and Anjany Sekuboyina and Fernando Navarro and Florian Kofler and Johannes C. Paetzold and Suprosanna Shit and Xiaobin Hu and Jana Lipková and Markus Rempfler and Marie Piraud and Jan Kirschke and Benedikt Wiestler and Zhiheng Zhang and Christian Hülsemeyer and Marcel Beetz and Florian Ettlinger and Michela Antonelli and Woong Bae and Míriam Bellver and Lei Bi and Hao Chen and Grzegorz Chlebus and Erik B. Dam and Qi Dou and Chi-Wing Fu and Bogdan Georgescu and Xavier Giró-i-Nieto and Felix Gruen and Xu Han and Pheng-Ann Heng and Jürgen Hesser and Jan Hendrik Moltz and Christian Igel and Fabian Isensee and Paul Jäger and Fucang Jia and Krishna Chaitanya Kaluva and Mahendra Khened and Ildoo Kim and Jae-Hun Kim and Sungwoong Kim and Simon Kohl and Tomasz Konopczynski and Avinash Kori and Ganapathy Krishnamurthi and Fan Li and Hongchao Li and Junbo Li and Xiaomeng Li and John Lowengrub and Jun Ma and Klaus Maier-Hein and Kevis-Kokitsi Maninis and Hans Meine and Dorit Merhof and Akshay Pai and Mathias Perslev and Jens Petersen and Jordi Pont-Tuset and Jin Qi and Xiaojuan Qi and Oliver Rippel and Karsten Roth and Ignacio Sarasua and Andrea Schenk and Zengming Shen and Jordi Torres and Christian Wachinger and Chunliang Wang and Leon Weninger and Jianrong Wu and Daguang Xu and Xiaoping Yang and Simon Chun-Ho Yu and Yading Yuan and Miao Yue and Liping Zhang and Jorge Cardoso and Spyridon Bakas and Rickmer Braren and Volker Heinemann and Christopher Pal and An Tang and Samuel Kadoury and Luc Soler and Bram {van Ginneken} and Hayit Greenspan and Leo Joskowicz and Bjoern Menze},
}


@article{TotalSegmentator,
author = {Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T. and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W. and Heye, Tobias and Boll, Daniel T. and Cyriac, Joshy and Yang, Shan and Bach, Michael and Segeroth, Martin},
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 La Foug{\`e}re, Christian and Nikolaou, Konstantin and Pfannenberg, Christina and Sch{\"o}lkopf, Bernhard and K{\"u}stner, Thomas and Cyran, Clemens and Rubin, Daniel},
  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 Gu, Sijing and Nikolaou, Konstantin and La Foug{\`e}re, Christian and Ye, Jin and He, Junjun and Peng, Yige and Bi, Lei 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 Freymann, John and Kirby, Justin and Koppel, Paul and Moore, Stephen and Phillips, Stephen and Maffitt, Derek and Pringle, Michael and Tarbox, Lawrence and Prior, Fred},
  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 Dinh-Tuan Tran and Xavier Baro and Hugo Jair Escalante and Sergio Escalera and Tyler Thomas and Zhen Xu},
  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}
}

