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Siamese few-shot network: a novel and efficient network for medical image segmentation

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Abstract

Few-shot learning is attracting more researchers due to its outstanding ability to find unseen classes with less data. Meanwhile, we noticed that medical data is difficult to collect and label, but there is a major need for higher accuracy in either organ segmentation or disease classification. Therefore, we propose a few-shot learning model with a Siamese core, the Siamese few-shot network (SFN) to improve medical image segmentation. To the beset of our knowledge, SFN is the first model to introduce few-shot learning combined with the Siamese idea to medical image segmentation. Furthermore, we also design a grid attention(GA) module to locally focus semantic information, especially in medical images. The results prove that our method outperforms the state-of-the-art model on abdominal organ segmentation for CT and MRI.

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Data Availability

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

References

  1. Ding Y, Yu X, Yang Y (2021) Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 1246–1254

  2. Souadih K, Belaid A, Salem DB, Conze P-H (2020) Automatic forensic identification using 3d sphenoid sinus segmentation and deep characterization. Medical & Biological Engineering & Computing 58(2):291–306

    Article  Google Scholar 

  3. Chlebus G, Meine H, Thoduka S, Abolmaali N, Van Ginneken B, Hahn HK, Schenk A (2019) Reducing inter-observer variability and interaction time of mr liver volumetry by combining automatic cnn-based liver segmentation and manual corrections. PloS one 14(5):0217228

    Article  Google Scholar 

  4. Ma Y, Bai S, An S, Liu W, Liu A, Zhen X, Liu X (2020) Transductive relation-propagation network for few-shot learning. In: IJCAI, pp 804–810

  5. Ding H, Zhang H, Jiang X (2022) Self-regularized prototypical network for few-shot semantic segmentation. Pattern Recognition, pp 109018

  6. Lu Q, Liu W, Zhuo Z, Li Y, Duan Y, Yu P, Qu L, Ye C, Liu Y (2022) A transfer learning approach to few-shot segmentation of novel white matter tracts. Med Image Anal 79:102454

    Article  Google Scholar 

  7. Munjal B, Flaborea A, Amin S, Tombari F, Galasso F (2022) Query-guided networks for few-shot fine-grained classification and person search. Pattern Recognition, pp 109049

  8. Han G, Ma J, Huang S, Chen L, Chang S-F (2022) Few-shot object detection with fully cross-transformer. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5321–5330

  9. Kaul P, Xie W, Zisserman A (2022) Label, verify, correct: a simple few shot object detection method. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 14237–14247

  10. Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Computing Surveys (CSUR) 53(3):1–34

    Article  Google Scholar 

  11. Yu Q, Dang K, Tajbakhsh N, Terzopoulos D, Ding X (2021) A location-sensitive local prototype network for few-shot medical image segmentation. In: 2021 IEEE 18th international symposium on biomedical imaging (ISBI), pp 262–266. IEEE

  12. Hansen S, Gautam S, Jenssen R, Kampffmeyer M (2022) Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Med Image Anal. 78:102385

    Article  Google Scholar 

  13. Wang Y, Xu Z, Tian J, Luo J, Shi Z, Zhang Y, Fan J, He Z (2022) Cross-domain few-shot learning for rare-disease skin lesion segmentation. In: ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1086–1090. IEEE

  14. Shen X, Zhang G, Lai H, Luo J, Lu J, Luo Y (2021) Poissonseg: Semi-supervised few-shot medical image segmentation via poisson learning. In: 2021 IEEE international conference on Bioinformatics and biomedicine (BIBM), pp 1513–1518. IEEE

  15. Lang C, Cheng G, Tu B, Han J (2022) Learning what not to segment: A new perspective on few-shot segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8057–8067

  16. Ye X, Leake D, Huibregtse W, Dalkilic M (2020) Applying class-to-class siamese networks to explain classifications with supportive and contrastive cases. In: International conference on case-based reasoning, pp 245–260. Springer

  17. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  18. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241. Springer

  19. Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Z, Li H (2021) Lcu-net: A novel low-cost u-net for environmental microorganism image segmentation. Pattern Recogn 115:107885

    Article  Google Scholar 

  20. Cao X, Lin Y (2021) Caggnet: crossing aggregation network for medical image segmentation. In: 2020 25th International conference on pattern recognition (ICPR), pp 1744–1750. IEEE

  21. Yan X, Tang H, Sun S, Ma H, Kong D, Xie X (2022) After-unet: axial fusion transformer unet for medical image segmentation. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 3971–3981

  22. Ouyang C, Biffi C, Chen C, Kart T, Qiu H, Rueckert D (2020) Self-supervision with superpixels: Training few-shot medical image segmentation without annotation. In: European conference on computer vision, pp 762–780. Springer

  23. Tian Z, Zhao H, Shu M, Yang Z, Li R, Jia J (2020) Prior guided feature enrichment network for few-shot segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence

  24. Li G, Jampani V, Sevilla-Lara L, Sun D, Kim J, Kim J (2021) Adaptive prototype learning and allocation for few-shot segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8334–8343

  25. Jiang M, Yuan Y, Wang Q (2018) Self-attention learning for person re-identification. In: BMVC, pp 204

  26. Zhang F, Ma B, Chang H, Shan S, Chen X (2019) Relation-aware multiple attention siamese networks for robust visual tracking. In: BMVC

  27. Sang H, Zhou Q, Zhao Y (2020) Pcanet: pyramid convolutional attention network for semantic segmentation. Image Vis Comput 103:103997

    Article  Google Scholar 

  28. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Supplementary material for ’eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the 2020 IEEE/CVF conference on computer vision and pattern recognition, IEEE, Seattle, WA, USA, pp 13–19

  29. Qin Z, Zhang P, Wu F, Li X (2021) Fcanet: frequency channel attention networks. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 783–792

  30. Guo M-H, Liu Z-N, Mu T-J, Hu S-M (2022) Beyond self-attention: external attention using two linear layers for visual tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence

  31. Rao Y, Zhao W, Zhu Z, Lu J, Zhou J (2021) Global filter networks for image classification. Adv Neural Inf Process Syst 34:980–993

    Google Scholar 

  32. Xia Z, Pan X, Song S, Li LE, Huang G (2022) Vision transformer with deformable attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4794–4803

  33. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  34. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the european conference on computer vision (ECCV), pp 3–19

  35. Pan X, Ge C, Lu R, Song S, Chen G, Huang Z, Huang G (2022) On the integration of self-attention and convolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 815–825

  36. Kavur AE, Selver MA, Dicle O, Barıs M, Gezer NS (2019) CHAOS - Combined (CT-MR) healthy abdominal organ segmentation challenge data. https://doi.org/10.5281/zenodo.3431873

  37. Landman B, Xu Z, Igelsias J, Styner M, Langerak T, Klein A (2015) Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge. In: Proc MICCAI multi-atlas labeling beyond cranial vault—workshop challenge, vol 5, pp 12

  38. Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  Google Scholar 

  39. Roy AG, Siddiqui S, Pölsterl S, Navab N, Wachinger C (2020) ’squeeze & excite’guided few-shot segmentation of volumetric images. Med Image Anal 59:101587

    Article  Google Scholar 

  40. Wang K, Liew JH, Zou Y, Zhou D, Feng J (2019) Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9197–9206

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant U2003208.

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    Authors and Affiliations

    1. College of Software Engineering, Xinjiang University, Urumqi, 830000, China

      Guangli Xiao, Shengwei Tian, Zhicheng Zhou & Xuanli Zeng

    2. Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, 830000, China

      Guangli Xiao, Shengwei Tian, Zhicheng Zhou & Xuanli Zeng

    3. Network Center, Xinjiang University, Urumqi, 830046, China

      Long Yu

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    1. Guangli Xiao
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    Correspondence to Shengwei Tian.

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    Xiao, G., Tian, S., Yu, L. et al. Siamese few-shot network: a novel and efficient network for medical image segmentation. Appl Intell 53, 17952–17964 (2023). https://doi.org/10.1007/s10489-022-04417-z

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