Skip to main content
SpringerLink
Log in

HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Semi-supervised learning methods have been attracting much attention in medical image segmentation due to the lack of high-quality annotation. To cope with the noise problem of pseudo-label in semi-supervised medical image segmentation and the limitations of contrastive learning applications, we propose a semi-supervised medical image segmentation framework, HPFG, based on hybrid pseudo-label and feature-guiding, which consists of a hybrid pseudo-label strategy and two different feature-guiding modules. The hybrid pseudo-label strategy uses the CutMix operation and an auxiliary network to enable the labeled images to guide the unlabeled images to generate high-quality pseudo-label and reduce the impact of pseudo-label noise. In addition, a feature-guiding encoder module based on feature-level contrastive learning is designed to guide the encoder to mine useful local and global image features, thus effectively enhancing the feature extraction capability of the model. At the same time, a feature-guiding decoder module based on adaptive class-level contrastive learning is designed to guide the decoder in better extracting class information, achieving intra-class affinity and inter-class separation, and effectively alleviating the class imbalance problem in medical datasets. Extensive experimental results show that the segmentation performance of the HPFG framework proposed in this paper outperforms existing semi-supervised medical image segmentation methods on three public datasets: ACDC, LIDC, and ISIC. Code is available at https://github.com/fakerlove1/HPFG.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Raghu M, Zhang C, Kleinberg J, et al (2019) Transfusion: understanding transfer learning for medical imaging[J]. Adv Neural Inform Process Syst 32. https://doi.org/10.48550/arXiv.1902.07208

  2. You C, Zhao R, Liu F, et al (2022) Class-aware generative adversarial transformers for medical image segmentation[J]. arXiv preprint arXiv:2201.10737. https://doi.org/10.48550/arXiv.2201.10737

  3. Isensee F, Petersen J, Klein A, et al (2018) nnu-net: self-adapting framework for u-net-based medical image segmentation[J]. arXiv preprint arXiv:1809.10486. https://doi.org/10.1007/978-3-658-25326-4_7

  4. Luo X, Chen J, Song T, et al (2021) Semi-supervised medical image segmentation through dual-task consistency[C]. Proc AAAI Conf Artif Intell 35(10):8801–8809. https://doi.org/10.48550/arXiv.2009.04448

  5. Basak H, Bhattacharya R, Hussain R, et al (2022) An embarrassingly simple consistency regularization method for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2202.00677. https://doi.org/10.48550/arXiv.2202.00677

  6. Wu Y, Ge Z, Zhang D et al (2022) Mutual consistency learning for semi-supervised medical image segmentation[J]. Med Image Anal 81:102530. https://doi.org/10.48550/arXiv.2112.02508

    Article  PubMed  Google Scholar 

  7. Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results[J]. Adv Neural Inform Process Syst 30. https://doi.org/10.48550/arXiv.1703.01780

  8. Lee DH (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks[C]//Workshop on challenges in representation learning. ICML 3(2):896

    Google Scholar 

  9. Zou Y, Zhang Z, Zhang H, et al (2020) Pseudoseg: designing pseudo labels for semantic segmentation[J]. arXiv preprint arXiv:2010.09713. https://doi.org/10.48550/arXiv.2010.09713

  10. Sohn K, Berthelot D, Carlini N et al (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence[J]. Adv Neural Inform Process Syst 33:596–608. https://doi.org/10.48550/arXiv.2001.07685

    Article  Google Scholar 

  11. Wang K, Zhan B, Zu C et al (2022) Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning[J]. Med Image Anal 79:102447. https://doi.org/10.1016/j.media.2022.102447

    Article  PubMed  Google Scholar 

  12. Liu Y, Wang W, Luo G et al (2022) A contrastive consistency semi-supervised left atrium segmentation model[J]. Comput Med Imaging Graph 99:102092. https://doi.org/10.1016/j.compmedimag.2022.102092

    Article  PubMed  Google Scholar 

  13. You C, Zhao R, Staib L H, et al (2022) Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part IV. Cham: Springer Nature Switzerland 639-652.https://doi.org/10.1007/978-3-031-16440-8_61

  14. Lei T, Zhang D, Du X et al (2022) Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network[J]. IEEE Trans Med Imaging. https://doi.org/10.1109/TMI.2022.3225687

    Article  PubMed  PubMed Central  Google Scholar 

  15. Miyato T, Maeda S, Koyama M et al (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE Trans Pattern Anal Mach Intell 41(8):1979–1993. https://doi.org/10.1109/TPAMI.2018.2858821

    Article  PubMed  Google Scholar 

  16. Qiao S, Shen W, Zhang Z, et al (2018) Deep co-training for semi-supervised image recognition[C]. Proc Eur Conf Comput Vision (ECCV) 135–152. https://doi.org/10.48550/arXiv.1803.05984

  17. Wang Z, Li T, Zheng J Q, et al (2023) When CNN meet with ViT: towards semi-supervised learning for multi-class medical image semantic segmentation[C]. Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII. Cham: Springer Nature Switzerland 424-441.https://doi.org/10.1007/978-3-031-25082-8_28

  18. Yeung M, Sala E, Schönlieb CB et al (2022) Unified focal loss: generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation[J]. Comput Med Imaging Graph 95:102026. https://doi.org/10.1016/j.compmedimag.2021.102026

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hu H, Wei F, Hu H et al (2021) Semi-supervised semantic segmentation via adaptive equalization learning[J]. Adv Neural Inform Process Syst 34:22106–22118

    Google Scholar 

  20. Armato SG III, McLennan G, Bidaut L et al (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans[J]. Med Phys 38(2):915–931. https://doi.org/10.1118/1.3528204

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen X, Yuan Y, Zeng G, et al (2021) Semi-supervised semantic segmentation with cross pseudo supervision[C]. Proc IEEE/CVF Conf Comput Vision Pattern Recog 2613–2622. https://doi.org/10.48550/arXiv.2106.01226

  22. Luo X, Hu M, Song T, et al (2022) Semi-supervised medical image segmentation via cross teaching between CNN and transformer[C]. Int Conf Med Imaging Deep Learn PMLR 820–833. https://doi.org/10.48550/arXiv.2112.04894

  23. Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929. https://doi.org/10.48550/arXiv.2010.11929

  24. Shen Z, Cao P, Yang H, et al (2023) Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2301.04465. https://doi.org/10.48550/arXiv.2301.04465

  25. Zhang Z, Tian C, Bai HX, Jiao Z, Tian X (2022) Discriminaive error prediction network for semi-supervised colon gland segmentation. Med Image Anal 79:102458. https://doi.org/10.1016/j.media.2022.102458

    Article  PubMed  Google Scholar 

  26. Zhao X, Qi Z, Wang S, et al (2023) RCPS: Rectified contrastive pseudo supervision for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2301.05500. https://doi.org/10.48550/arXiv.2301.05500

  27. Wang X, Zhang R, Shen C, et al (2021) Dense contrastive learning for self-supervised visual pre-training[C]. Proc IEEE/CVF Conf Comput Vision Pattern Recog 3024–3033. https://doi.org/10.48550/arXiv.2011.09157

  28. Chen T, Kornblith S, Norouzi M, et al (2020) A simple framework for contrastive learning of visual representations[C]. Int Conf Mach Learn PMLR 1597–1607. https://doi.org/10.48550/arXiv.2002.05709

  29. He K, Fan H, Wu Y, et al (2020) Momentum contrast for unsupervised visual representation learning[C]. Proc IEEE/CVF Conf Comput Vision Pattern Recog 9729–9738. https://doi.org/10.48550/arXiv.1911.05722

  30. Chaitanya K, Erdil E, Karani N et al (2020) Contrastive learning of global and local features for medical image segmentation with limited annotations[J]. Adv Neural Inform Process Syst 33:12546–12558. https://doi.org/10.48550/arXiv.2006.10511

    Article  Google Scholar 

  31. Hu X, Zeng D, Xu X, et al (2021) Semi-supervised contrastive learning for label-efficient medical image segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part II 24. Springer International Publishing 481-490.https://doi.org/10.1007/978-3-030-87196-3_45

  32. Chaitanya K, Erdil E, Karani N, et al (2023) Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation[J]. Med Image Anal 102792. https://doi.org/10.1016/j.media.2023.102792

  33. Wu Y, Wu Z, Wu Q, et al (2022) Exploring smoothness and class-separation for semi-supervised medical image segmentation[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2022: 25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part V. Cham: Springer Nature Switzerland 34-43.https://doi.org/10.1007/978-3-031-16443-9_4

  34. Wang T, Lu J, Lai Z, et al (2022) Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation[C]. Proc Thirty-First Int Joint Conf Artif Intell IJCAI 1444–1450. https://doi.org/10.1142/S0129065722500162

  35. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing 234-241.https://doi.org/10.1007/978-3-319-24574-4_28

  36. Yun S, Han D, Oh S J, et al (2019) Cutmix: regularization strategy to train strong classifiers with localizable features[C]//Proceedings of the IEEE/CVF international conference on computer vision 6023–6032. https://doi.org/10.48550/arXiv.1905.04899

  37. Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]//2016 fourth international conference on 3D vision (3DV). IEEE 565–571. https://doi.org/10.1109/3DV.2016.79

  38. Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding[J]. arXiv preprint arXiv:1807.03748. https://doi.org/10.48550/arXiv.1807.03748

  39. Bernard O, Lalande A, Zotti C et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?[J]. IEEE Trans Med Imaging 37(11):2514–2525. https://doi.org/10.1109/TMI.2018.2837502

    Article  PubMed  Google Scholar 

  40. Codella N C F, Gutman D, Celebi M E, et al (2018) Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC)[C]//2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE 168-172.https://doi.org/10.1109/ISBI.2018.8363547

  41. Selvaraju R R, Cogswell M, Das A, et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization[C]. Proc IEEE Int Conf Comput Vision 618–626. https://doi.org/10.1007/s11263-019-01228-7

Download references

Acknowledgements

The authors thank the editors and reviewers for their valuable comments that improve the paper’s quality.

Funding

This work is supported by the Fundamental Research Program of Shanxi Province (No.202203021211177).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Taiyuan University of Technology, Jinzhong, 030600, China

    Feixiang Li, Ailian Jiang, Mengyang Li, Cimei Xiao & Wei Ji

Authors
  1. Feixiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ailian Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mengyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cimei Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ailian Jiang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, F., Jiang, A., Li, M. et al. HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding. Med Biol Eng Comput 62, 405–421 (2024). https://doi.org/10.1007/s11517-023-02946-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02946-4

Keywords

Search

Navigation