GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data
Authors: 
Haochen Zhao, Hui Meng, Deqian Yang, Xiaozheng Xie, Xiaoze Wu, Qingfeng Li, Jianwei NiuAuthors Info & Claims
Pages 886 - 895
Abstract
Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation.Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled data.To address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs.A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same class.On this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated pseudo-labels.Furthermore, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-the-art performance.
References
[1]
Phuoc-Nguyen Bui, Duc-Tai Le, Junghyun Bum, Seongho Kim, Su Jeong Song, and Hyunseung Choo. 2023. Semi-supervised learning with fact-forcing for medical image segmentation. IEEE Access (2023).
[2]
Duowen Chen, Yunhao Bai, Wei Shen, Qingli Li, Lequan Yu, and Yan Wang. 2023. MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 23869--23878.
[3]
Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. 2021. Semisupervised semantic segmentation with cross pseudo supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2613-- 2622.
[4]
Zenggui Chen and Zhouhui Lian. 2022. Semi-supervised Semantic Segmentation via Prototypical Contrastive Learning. In Proceedings of the 30th ACM International Conference on Multimedia. 6696--6705.
[5]
Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Assisted Intervention-- MICCAI 2016: 19th International Conference, Athens, Greece, October 17--21, 2016, Proceedings, Part II 19. Springer, 424--432.
[6]
Per-Erik Danielsson. 1980. Euclidean distance mapping. Computer Graphics and image processing 14, 3 (1980), 227--248.
[7]
Yue Duan, Lei Qi, Lei Wang, Luping Zhou, and Yinghuan Shi. 2022. Rda: Reciprocal distribution alignment for robust semi-supervised learning. In European Conference on Computer Vision. Springer, 533--549.
[8]
Yue Duan, Zhen Zhao, Lei Qi, Lei Wang, Luping Zhou, Yinghuan Shi, and Yang Gao. 2024. MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regularization. IEEE transactions on neural networks and learning systems 35, 6 (2024), 8441--8455.
[9]
Yue Duan, Zhen Zhao, Lei Qi, Luping Zhou, LeiWang, and Yinghuan Shi. 2023. Towards semi-supervised learning with non-random missing labels. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 16121--16131.
[10]
Yue Duan, Zhen Zhao, Lei Qi, Luping Zhou, Lei Wang, and Yinghuan Shi. 2024. Roll with the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 11829--11837.
[11]
Hongjian Gao, Mengyao Lyu, Xinyue Zhao, Fan Yang, and Xiangzhi Bai. 2023. Contour-aware network with class-wise convolutions for 3D abdominal multiorgan segmentation. Medical Image Analysis 87 (2023), 102838.
[12]
Negin Ghamsarian, Javier Gamazo Tejero, Pablo Márquez-Neila, Sebastian Wolf, Martin Zinkernagel, Klaus Schoeffmann, and Raphael Sznitman. 2023. Domain Adaptation for Medical Image Segmentation Using Transformation-Invariant Selftraining. In International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 331--341.
[13]
Jie Hu, Chen Chen, Liujuan Cao, Shengchuan Zhang, Annan Shu, Guannan Jiang, and Rongrong Ji. 2023. Pseudo-label Alignment for Semi-supervised Instance Segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 16337--16347.
[14]
Huimin Huang, Yawen Huang, Shiao Xie, Lanfen Lin, Tong Ruofeng, Yen-wei Chen, Yuexiang Li, and Yefeng Zheng. 2023. Semi-Supervised Convolutional Vision Transformer with Bi-Level Uncertainty Estimation for Medical Image Segmentation. In Proceedings of the 31st ACM International Conference on Multimedia. 5214--5222.
[15]
Wei Huang, Chang Chen, Zhiwei Xiong, Yueyi Zhang, Xuejin Chen, Xiaoyan Sun, and Feng Wu. 2022. Semi-supervised neuron segmentation via reinforced consistency learning. IEEE Transactions on Medical Imaging 41, 11 (2022), 3016-- 3028.
[16]
Yuanfeng Ji, Haotian Bai, Jie Yang, Chongjian Ge, Ye Zhu, Ruimao Zhang, Zhen Li, Lingyan Zhang, Wanling Ma, Xiang Wan, et al. 2022. AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation. arXiv preprint arXiv:2206.08023 (2022).
[17]
Peixia Li, Pulak Purkait, Thalaiyasingam Ajanthan, Majid Abdolshah, Ravi Garg, Hisham Husain, Chenchen Xu, Stephen Gould, Wanli Ouyang, and Anton Van Den Hengel. 2023. Semi-supervised semantic segmentation under label noise via diverse learning groups. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 1229--1238.
[18]
Shuailin Li, Chuyu Zhang, and Xuming He. 2020. Shape-aware semi-supervised 3D semantic segmentation for medical images. In Medical Image Computing and Computer Assisted Intervention--MICCAI 2020: 23rd International Conference, Lima, Peru, October 4--8, 2020, Proceedings, Part I 23. Springer, 552--561.
[19]
Chen Liang, Wenguan Wang, Jiaxu Miao, and Yi Yang. 2022. GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 31360--31375. https://proceedings.neurips.cc/paper_files/paper/2022/file/ cb1c4782f159b55380b4584671c4fd88-Paper-Conference.pdf
[20]
Yiqun Lin, Huifeng Yao, Zezhong Li, Guoyan Zheng, and Xiaomeng Li. 2022. Calibrating label distribution for class-imbalanced barely-supervised knee segmentation. In International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 109--118.
[21]
Hao Liu, Bin Chen, Bo Wang, Chunpeng Wu, Feng Dai, and Peng Wu. 2022. Cycle self-training for semi-supervised object detection with distribution consistency reweighting. In Proceedings of the 30th ACM International Conference on Multimedia. 6569--6578.
[22]
Han Liu, Zhoubing Xu, Riqiang Gao, Hao Li, Jianing Wang, Guillaume Chabin, Ipek Oguz, and Sasa Grbic. 2023. COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training. arXiv preprint arXiv:2304.14030 (2023).
[23]
Xiangde Luo, Jieneng Chen, Tao Song, and Guotai Wang. 2021. Semi-supervised medical image segmentation through dual-task consistency. In Proceedings of the AAAI conference on artificial intelligence, Vol. 35. 8801--8809.
[24]
Xiangde Luo, Guotai Wang, Wenjun Liao, Jieneng Chen, Tao Song, Yinan Chen, Dimitris N. Metaxas Zhang, Shichuan, and Shaoting Zhang. 2022. Semi- Supervised Medical Image Segmentation via Uncertainty Rectified Pyramid Consistency. Medical Image Analysis 80 (2022), 102517.
[25]
Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, et al. 2023. Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. arXiv preprint arXiv:2308.05862 (2023).
[26]
Jun Ma, Yao Zhang, Song Gu, Cheng Ge, Shihao Ma, Adamo Young, Cheng Zhu, Kangkang Meng, Xin Yang, Ziyan Huang, Fan Zhang, Wentao Liu, YuanKe Pan, Shoujin Huang, Jiacheng Wang, Mingze Sun, Weixin Xu, Dengqiang Jia, Jae Won Choi, Natália Alves, Bram de Wilde, Gregor Koehler, Yajun Wu, Manuel Wiesenfarth, Qiongjie Zhu, Guoqiang Dong, Jian He, the FLARE Challenge Consortium, and Bo Wang. 2023. Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge. arXiv preprint arXiv:2308.05862 (2023).
[27]
Hui Meng, Haochen Zhao, Ziniu Yu, Qingfeng Li, and Jianwei Niu. 2022. Uncertainty-aware Mean Teacher Framework with Inception and Squeeze-and- Excitation Block for MICCAI FLARE22 Challenge. In MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation. Springer, 245--259.
[28]
Yanda Meng, Xu Chen, Hongrun Zhang, Yitian Zhao, Dongxu Gao, Barbra Hamill, Godhuli Patri, Tunde Peto, Savita Madhusudhan, and Yalin Zheng. 2022. Shape-aware weakly/semi-supervised optic disc and cup segmentation with regional/marginal consistency. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 524--534.
[29]
Juzheng Miao, Cheng Chen, Furui Liu, Hao Wei, and Pheng-Ann Heng. 2023. Caussl: Causality-inspired semi-supervised learning for medical image segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 21426--21437.
[30]
T.K. Moon. 1996. The expectation-maximization algorithm. IEEE Signal Processing Magazine 13, 6 (1996), 47--60. https://doi.org/10.1109/79.543975
[31]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).
[32]
Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary Egan, and Mehrtash Harandi. 2023. Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation. Nature Machine Intelligence 5, 7 (2023), 724--738.
[33]
Zhiqiang Shen, Peng Cao, Hua Yang, Xiaoli Liu, Jinzhu Yang, and Osmar R Zaiane. 2023. Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation. arXiv preprint arXiv:2301.04465 (2023).
[34]
Yucheng Shu, Hengbo Li, Bin Xiao, Xiuli Bi, and Weisheng Li. 2022. Cross-mix monitoring for medical image segmentation with limited supervision. IEEE Transactions on Multimedia (2022).
[35]
Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017).
[36]
Bethany H Thompson, Gaetano Di Caterina, and Jeremy P Voisey. 2022. Pseudolabel refinement using superpixels for semi-supervised brain tumour segmentation. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 1--5.
[37]
Haonan Wang and Xiaomeng Li. 2023. Dhc: Dual-debiased heterogeneous cotraining framework for class-imbalanced semi-supervised medical image segmentation. In International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 582--591.
[38]
Zhou Wang and Alan C Bovik. 2009. Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE signal processing magazine 26, 1 (2009), 98--117.
[39]
Zhihui Wang, Shijie Wang, Shuhui Yang, Haojie Li, Jianjun Li, and Zezhou Li. 2020. Weakly supervised fine-grained image classification via guassian mixture model oriented discriminative learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9749--9758.
[40]
Yixuan Wu, Jintai Chen, Jiahuan Yan, Yiheng Zhu, Danny Z Chen, and Jian Wu. 2023. GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels. In Proceedings of the 31st ACM International Conference on Multimedia. 463--471.
[41]
Yingda Xia, Dong Yang, Zhiding Yu, Fengze Liu, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, and Holger Roth. 2020. Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Medical image analysis 65 (2020), 101766.
[42]
Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David Clifton, S Kevin Zhou, Lawrence Staib, and James Duncan. 2024. Rethinking semi-supervised medical image segmentation: A variance-reduction perspective. Advances in Neural Information Processing Systems 36 (2024).
[43]
Lequan Yu, ShujunWang, Xiaomeng Li, Chi-Wing Fu, and Pheng-Ann Heng. 2019. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In Medical Image Computing and Computer Assisted Intervention-- MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13--17, 2019, Proceedings, Part II 22. Springer, 605--613.
[44]
Jianlong Yuan, Jinchao Ge, Zhibin Wang, and Yifan Liu. 2023. Semi-supervised semantic segmentation with mutual knowledge distillation. In Proceedings of the 31st ACM International Conference on Multimedia. 5436--5444.
[45]
Hongcheng Zhang, Xu Zhao, and DongqiWang. 2022. Semi-supervised Learning for Multi-label Video Action Detection. In Proceedings of the 30th ACM International Conference on Multimedia. 2124--2134.
[46]
Wei Zhang, Xiaohong Zhang, Sheng Huang, Yuting Lu, and KunWang. 2022. PixelSeg: Pixel-by-Pixel Stochastic Semantic Segmentation for Ambiguous Medical Images. In Proceedings of the 30th ACM International Conference on Multimedia. 4742--4750.
[47]
Wei Zhang, Xiaohong Zhang, Sheng Huang, Yuting Lu, and Kun Wang. 2022. A probabilistic model for controlling diversity and accuracy of ambiguous medical image segmentation. In Proceedings of the 30th ACM International Conference on Multimedia. 4751--4759.
[48]
Yizhe Zhang, Lin Yang, Jianxu Chen, Maridel Fredericksen, David P Hughes, and Danny Z Chen. 2017. Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11--13, 2017, Proceedings, Part III 20. Springer, 408--416.
[49]
Haochen Zhao, Jianwei Niu, Hui Meng, Yong Wang, Qingfeng Li, and Ziniu Yu. 2022. Focal U-Net: A focal self-attention based U-Net for breast lesion segmentation in ultrasound images. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 1506--1511.
[50]
Qianfei Zhao, Lanfeng Zhong, Jianghong Xiao, Jingbo Zhang, Yinan Chen, Wenjun Liao, Shaoting Zhang, and GuotaiWang. 2023. Efficient Multi-Organ Segmentation from 3D Abdominal CT Images with Lightweight Network and Knowledge Distillation. IEEE Transactions on Medical Imaging (2023).
[51]
Xiangyu Zhao, Zengxin Qi, Sheng Wang, Qian Wang, Xuehai Wu, Ying Mao, and Lichi Zhang. 2023. Rcps: Rectified contrastive pseudo supervision for semisupervised medical image segmentation. arXiv preprint arXiv:2301.05500 (2023).
[52]
Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot Fishman, and Alan Yuille. 2019. Semi-supervised 3D abdominal multi-organ segmentation via deep multi-planar co-training. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 121--140.
Index Terms
- GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data
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October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
- General Chairs:
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- Susanne Boll,
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- Liang Zheng,
- Vivek K. Singh,
- Pablo Cesar,
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- Dong Xu
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