Skip to main content

Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images

  • Conference paper
  • First Online:
Pattern Recognition (ICPR 2024)

Abstract

Medical image segmentation is one of the domains where sufficient annotated data is not available. This necessitates the application of low-data frameworks like few-shot learning. Contemporary prototype-based frameworks often do not account for the variation in features within the support and query images, giving rise to a large variance in prototype alignment. In this work, we adopt a prototype-based self-supervised one-way one-shot learning framework using pseudo-labels generated from superpixels to learn the semantic segmentation task itself. We use a correlation-based probability score to generate a dynamic prototype for each query pixel from the bag of prototypes obtained from the support feature map. This weighting scheme helps to give a higher weightage to contextually related prototypes. We also propose a quadrant masking strategy in the downstream segmentation task by utilizing prior domain information to discard unwanted false positives. We present extensive experimentations and evaluations on abdominal CT and MR datasets to show that the proposed simple but potent framework performs at par with the state-of-the-art methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Amac, M., Sencan, A., Baran, O., Ikizler-Cinbis, N., Cinbis, R.: MaskSplit: self-supervised meta-learning for few-shot semantic segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 428–438. IEEE Computer Society, Los Alamitos, CA, USA (2022). https://doi.org/10.1109/WACV51458.2022.00050, https://doi.ieeecomputersociety.org/10.1109/WACV51458.2022.00050

  2. Araslanov, N., Roth, S.: Self-supervised augmentation consistency for adapting semantic segmentation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15379–15389. IEEE Computer Society, Los Alamitos, CA, USA (2021). https://doi.org/10.1109/CVPR46437.2021.01513, https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.01513

  3. Bhunia, A.K., Bhunia, A.K., Ghose, S., Das, A., Roy, P.P., Pal, U.: A deep one-shot network for query-based logo retrieval. Pattern Recogn. 96, 106965 (2019). https://doi.org/10.1016/j.patcog.2019.106965

    Article  Google Scholar 

  4. Chen, J., Gao, B.B., Lu, Z., Xue, J.H., Wang, C., Liao, Q.: APANet: adaptive prototypes alignment network for few-shot semantic segmentation. IEEE Trans. Multimedia 25, 1–1 (2022). https://doi.org/10.1109/TMM.2022.3174405

  5. Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: Self-supervised learning for medical image analysis using image context restoration. Med. Image Anal. 58, 101539 (2019). https://doi.org/10.1016/j.media.2019.101539

    Article  Google Scholar 

  6. Ding, H., Sun, C., Tang, H., Cai, D., Yan, Y.: Few-shot medical image segmentation with cycle-resemblance attention. In: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2487–2496. IEEE Computer Society, Los Alamitos, CA, USA (Jan 2023).https://doi.org/10.1109/WACV56688.2023.00252, https://doi.ieeecomputersociety.org/10.1109/WACV56688.2023.00252

  7. Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: British Machine Vision Conference (2018)

    Google Scholar 

  8. Fan, Q., Pei, W., Tai, Y.W., Tang, C.K.: Self-support few-shot semantic segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022, pp. 701–719. Springer Nature Switzerland, Cham (2022)

    Chapter  Google Scholar 

  9. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  10. Gansbeke, W.V., Vandenhende, S., Georgoulis, S., Gool, L.V.: Unsupervised semantic segmentation by contrasting object mask proposals. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10032–10042. IEEE Computer Society, Los Alamitos, CA, USA (oct 2021). https://doi.org/10.1109/ICCV48922.2021.00990, https://doi.ieeecomputersociety.org/10.1109/ICCV48922.2021.00990

  11. Gao, Z., et al.: Unsupervised representation learning for tissue segmentation in histopathological images: from global to local contrast. IEEE Trans. Medical Imaging 41(12), 3611–3623 (2022). https://doi.org/10.1109/TMI.2022.3191398

  12. Guha Roy, A., Siddiqui, S., Pölsterl, S., Navab, N., Wachinger, C.: ‘squeeze & excite’ guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020). https://doi.org/10.1016/j.media.2019.101587

    Article  Google Scholar 

  13. Guizilini, V., Ramos, F.: Online self-supervised segmentation of dynamic objects. In: 2013 IEEE International Conference on Robotics and Automation, pp. 4720–4727 (2013). https://doi.org/10.1109/ICRA.2013.6631249

  14. He, H., Zhang, J., Thuraisingham, B., Tao, D.: Progressive one-shot human parsing. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Event, February 2-9, 2021, pp. 1522–1530. AAAI Press (2021). https://ojs.aaai.org/index.php/AAAI/article/view/16243

  15. Hoyer, L., Dai, D., Chen, Y., Koring, A., Saha, S., Gool, L.V.: Three ways to improve semantic segmentation with self-supervised depth estimation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11125–11135. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2021). https://doi.org/10.1109/CVPR46437.2021.01098, https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.01098

  16. Ji, X., Vedaldi, A., Henriques, J.: Invariant information clustering for unsupervised image classification and segmentation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9864–9873. IEEE Computer Society, Los Alamitos, CA, USA (Nov 2019). https://doi.org/10.1109/ICCV.2019.00996, https://doi.ieeecomputersociety.org/10.1109/ICCV.2019.00996

  17. Kavur, A.E., et al.: CHAOS challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021). https://doi.org/10.1016/j.media.2020.101950

    Article  Google Scholar 

  18. Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., Kim, J.: Adaptive prototype learning and allocation for few-shot segmentation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8330–8339. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2021). https://doi.org/10.1109/CVPR46437.2021.00823, https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.00823

  19. Liu, J., Bao, Y., Xie, G., Xiong, H., Sonke, J., Gavves, E.: Dynamic prototype convolution network for few-shot semantic segmentation. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11543–11552. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2022). https://doi.org/10.1109/CVPR52688.2022.01126, https://doi.ieeecomputersociety.org/10.1109/CVPR52688.2022.01126

  20. Liu, W., Zhang, C., Ding, H., Hung, T.Y., Lin, G.: Few-shot segmentation with optimal transport matching and message flow. IEEE Trans. Multimedia 1–12 (2022). https://doi.org/10.1109/TMM.2022.3187855

  21. Liu, Y., Zhang, X., Zhang, S., He, X.: Part-aware prototype network for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 142–158. Springer, Cham (2020)

    Google Scholar 

  22. Okazawa, A.: Interclass prototype relation for few-shot segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022, pp. 362–378. Springer Nature Switzerland, Cham (2022)

    Chapter  Google Scholar 

  23. Ouali, Y., Hudelot, C., Tami, M.: Autoregressive unsupervised image segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12352, pp. 142–158. Springer, Cham (2020)

    Google Scholar 

  24. Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with Superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 762–780. Springer, Cham (2020)

    Google Scholar 

  25. Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervised learning for few-shot medical image segmentation. IEEE Trans. Med. Imaging 41(7), 1837–1848 (2022). https://doi.org/10.1109/TMI.2022.3150682

    Article  Google Scholar 

  26. Rakelly, K., Shelhamer, E., Darrell, T., Efros, A.A., Levine, S.: Conditional networks for few-shot semantic segmentation. In: International Conference on Learning Representations (2018)

    Google Scholar 

  27. Rakelly, K., Shelhamer, E., Darrell, T., Efros, A.A., Levine, S.: Few-shot segmentation propagation with guided networks. arxiv preprint arxiv:abs/1806.07373 (2018)

  28. Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: British Machine Vision Conference 2017, BMVC 2017, London, UK, September 4-7, 2017. BMVA Press (2017)

    Google Scholar 

  29. Siam, M., Oreshkin, B., Jagersand, M.: AMP: adaptive masked proxies for few-shot segmentation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5248–5257. IEEE Computer Society, Los Alamitos, CA, USA (Nov 2019). https://doi.org/10.1109/ICCV.2019.00535, https://doi.ieeecomputersociety.org/10.1109/ICCV.2019.00535

  30. Siam, M., Oreshkin, B.N.: Adaptive masked weight imprinting for few-shot segmentation. In: Workshop at the International Conference on Learning Representations (ICLR) (2019)

    Google Scholar 

  31. Singh, S., et al.: Self-supervised feature learning for semantic segmentation of overhead imagery. In: British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3-6, 2018. pp. 102. BMVA Press (2018). http://bmvc2018.org/contents/papers/0345.pdf

  32. Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., Zhen, X.: Few-shot semantic segmentation with democratic attention networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 730–746. Springer, Cham (2020)

    Google Scholar 

  33. Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: PANet: few-shot image semantic segmentation with prototype alignment. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9196–9205 (2019). https://doi.org/10.1109/ICCV.2019.00929

  34. Wu, H., Xiao, F., Liang, C.: Dual contrastive learning with anatomical auxiliary supervision for few-shot medical image segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022, pp. 417–434. Springer Nature Switzerland, Cham (2022)

    Chapter  Google Scholar 

  35. Yang, B., Liu, C., Li, B., Jiao, J., Ye, Q.: Prototype mixture models for few-shot semantic segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 763–778. Springer, Cham (2020)

    Google Scholar 

  36. Zhang, B., Xiao, J., Qin, T.: Self-guided and cross-guided learning for few-shot segmentation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8308–8317. IEEE Computer Society, Los Alamitos, CA, USA (Jun 2021). https://doi.org/10.1109/CVPR46437.2021.00821, https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.00821

  37. Zhang, K., Zheng, Y., Deng, X., Jia, W., Lian, J., Chen, X.: Guided networks for few-shot image segmentation and fully connected CRFs. Electronics 9(9), 1508 (2020). https://doi.org/10.3390/electronics9091508, https://www.mdpi.com/2079-9292/9/9/1508

  38. Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: SG-One: similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern. 50(9), 3855–3865 (2020). https://doi.org/10.1109/TCYB.2020.2992433

    Article  Google Scholar 

  39. Zhu, K., Zhai, W., Zha, Z., Cao, Y.: Self-supervised tuning for few-shot segmentation. In: International Joint Conference on Artificial Intelligence (2020). https://api.semanticscholar.org/CorpusID:215744862

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siladittya Manna .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Manna, S., Bhattacharya, S., Pal, U. (2025). Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15310. Springer, Cham. https://doi.org/10.1007/978-3-031-78192-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78192-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78191-9

  • Online ISBN: 978-3-031-78192-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics