Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, et al. Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inf Fus. 2023;90:316–52. https://doi.org/10.1016/j.inffus.2022.09.031.
Han K, Sheng VS, Song Y, Liu Y, Qiu C, Ma S, et al. Deep semi-supervised learning for medical image segmentation: a review. Expert Syst Appl. 2024;245:123052. https://doi.org/10.1016/j.eswa.2023.123052.
Zhang Z, Ran R, Tian C, Zhou H, Li X, Yang F, et al. Self-aware and cross-sample prototypical learning for semi-supervised medical image segmentation. International Conference on medical image computing and computer-assisted intervention. 2023;192–201. https://doi.org/10.1007/978-3-031-43895-0_18.
Liu X, Li W, Yuan Y. Diffrect: Latent diffusion label rectification for semi-supervised medical image segmentation. International Conference on medical image computing and computer-assisted intervention. Cham: Springer Nature Switzerland 2024:56-66. https://doi.org/10.1007/978-3-031-72390-2_6
Basak H, Yin Z. Pseudo-label guided contrastive learning for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023;19786–19797. https://doi.org/10.1109/CVPR52729.2023.01895.
Su J, Luo Z, Lian S, Lin D, Li S. Mutual learning with reliable pseudo label for semi-supervised medical image segmentation. Med Image Anal. 2024;94:103111. https://doi.org/10.1016/j.media.2024.103111.
Zhang Y, Jiao R, Liao Q, Li D, Zhang J. Uncertainty-guided mutual consistency learning for semi-supervised medical image segmentation. Artif Intell Med. 2023;138:102476. https://doi.org/10.1016/j.artmed.2022.102476.
Hu M, Yin J, Ma Z, Ma J, Zhu F, Wu B, et al. beta-FFT: Nonlinear interpolation and differentiated training strategies for semi-supervised medical image segmentation. In: Proceedings of the computer vision and pattern recognition conference. 2025;30839–30849. https://doi.org/10.1109/CVPR52734.2025.02872.
Zhang B, Wang Y, Hou W, Wu H, Wang J, Okumura M, et al. Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling. Adv Neural Inf Process Syst. 2021;34:18408–19.
Wang Y, Wang H, Shen Y, Fei J, Li W, Jin G, et al. Semi-supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022;4248–4257. https://doi.org/10.1109/CVPR52688.2022.00421.
Chen F, Fei J, Chen Y, Huang C. Decoupled consistency for semi-supervised medical image segmentation. International conference on medical image computing and computer-assisted intervention. 2023;551–561. https://doi.org/10.1007/978-3-031-43907-0_53.
Wang Y, Xiao B, Bi X, Li W, Gao X. Mcf: Mutual correction framework for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023;15651–15660. https://doi.org/10.1109/CVPR52729.2023.01502.
Ma Q, Zhang J, Qi L, Yu Q, Shi Y, Gao Y. Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024;11642–11651. https://doi.org/10.1109/CVPR52733.2024.01106.
Chi H, Pang J, Zhang B, Liu W. Adaptive bidirectional displacement for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024;4070–4080. https://doi.org/10.1109/CVPR52733.2024.00390.
Bai Y, Chen D, Li Q, Shen W, Wang Y. Bidirectional copy-paste for semi-supervised medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023;11514–11524. https://doi.org/10.1109/CVPR52729.2023.01108.
Dong M, Yang A, Wang Z, Li D, Yang J, Zhao R. Uncertainty-aware consistency learning for semi-supervised medical image segmentation. Knowl-Based Syst. 2025;309:112890. https://doi.org/10.1016/j.knosys.2024.112890.
Lu L, Yin M, Fu L, Yang F. Uncertainty-aware pseudo-label and consistency for semi-supervised medical image segmentation. Biomed Signal Process Control. 2023;79:104203. https://doi.org/10.1016/j.bspc.2022.104203.
Wu Y, Xu M, Ge Z, Cai J, Zhang L, (2021) Semi-supervised left atrium segmentation with mutual consistency training. Medical Image Computing and Computer-Assisted Intervention–MICCAI,. 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24. Springer International Publishing. 2021;297–306. https://doi.org/10.1007/978-3-030-87196-3_28.
Luo X, Hu M, Song T, Wang G, Zhang S. Semi-supervised medical image segmentation via cross teaching between CNN and transformer. International Conference on medical imaging with deep learning. PMLR 2022:820-833. https://doi.org/10.48550/arXiv.2112.04894
Zhao Z, Wang H, Lei T, Wang X, Shen X, Yao H. Balanced feature fusion collaborative training for semi-supervised medical image segmentation. Pattern Recogn. 2025;157:110856. https://doi.org/10.1016/j.patcog.2024.110856.
Wang J, Ruan D, Li Y, Wang Z, Wu Y, Tan T, et al. Data augmentation strategies for semi-supervised medical image segmentation. Pattern Recogn. 2025;159:111116. https://doi.org/10.1016/j.patcog.2024.111116.
Zeng Q, Xie Y, Lu Z, Lu M, Zhang J, Zhou Y, et al. Consistency-guided differential decoding for enhancing semi-supervised medical image segmentation. IEEE Trans Med Imaging. 2024;44(1):44–56. https://doi.org/10.1109/TMI.2024.3429340.
Khadidos A, Sanchez V, Li CT. Weighted level set evolution based on local edge features for medical image segmentation. IEEE Trans Image Process. 2017;26(4):1979–91. https://doi.org/10.1109/TIP.2017.2666042.
Article MathSciNet Google Scholar
Tang M, Valipour S, Zhang Z, Cobzas D, Jagersand M (2017) A deep level set method for image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA. and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings 3. Springer International Publishing. 2017;126–134. https://doi.org/10.1007/978-3-319-67558-9_15.
Luo X, Liao W, Chen J, Song T, Chen Y, Zhang S, et al. Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. International Conference on medical image computing and computer-assisted intervention. 2021;318–329. https://doi.org/10.1007/978-3-030-87196-3_30.
Ronneberger O, Fischer P, Brox T, (2015) U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI,. 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. Springer international publishing. 2015;234–241. https://doi.org/10.1007/978-3-319-24574-4_28.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. Proc IEEE Conf Comput Vis Pattern Recognit. 2016;770–780. https://doi.org/10.1109/CVPR.2016.90.
Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Nature methods 2022;18(2): 203-211. https://doi.org/10.1038/s41592-020-01008-z
Isensee F, Jaeger P.F, Kohl S.A, Petersen J, Maier-Hein K.H. Swin-unet: Unet-like pure transformer for medical image segmentation European conference on computer vision 2021:205-218. https://doi.org/10.1007/978-3-031-25066-8_90
Chen X, Yuan Y, Zeng G, Wang J. Semi-supervised semantic segmentation with cross pseudo supervision. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021;2613–2622. https://doi.org/10.1109/CVPR46437.2021.00264.
Laine S, Aila T. Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 2016.https://doi.org/10.48550/arXiv.1610.02242
Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved? IEEE Trans Med Imaging. 2018;2514–2525. https://doi.org/10.1109/TMI.2018.2837502.
Litjens G, Toth R, Van De Ven W, Hoeks C, Kerkstra S, Van Ginneken B, et al. Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Med Image Anal. 2014;18:359–73. https://doi.org/10.1016/j.media.2013.12.002.
Yang Y, Sun G, Zhang T, Wang R, Su J. Semi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation. Med Image Anal. 2025;101:103450. https://doi.org/10.1016/j.media.2024.103450.
Zhao Z, Wang Z, Wang L, Yu D, Yuan Y, Zhou L. Alternate diverse teaching for semi-supervised medical image segmentation. European Conference on Computer Vision. 2025;227–243. https://doi.org/10.1007/978-3-031-72652-1_14.
Wu Y, Wu Z, Wu Q, Ge Z, Cai J. Exploring smoothness and class-separation for semi-supervised medical image segmentation. International Conference on medical image computing and computer-assisted intervention. 2022;34–43. https://doi.org/10.1007/978-3-031-16443-9_4.
Comments (0)