Abstract
Recently, few-shot medical image segmentation approaches have been extensively explored to tackle the challenge of scarce labeled data in medical images. The majority of existing methods employ prototype-based techniques and have achieved promising results. However, conventional prototype extraction approaches inherently lead to loss of spatial information, thus degrading model performance, an issue further aggravated in medical images with large background regions. In this work, we propose a self-guided local prototype generation module (SLP), which progressively splits support masks into sub-mask, thereby producing a set of local prototype that preserve richer support image information. Moreover, in order to take full advantage of the information contained within the prototype sets during the iterative process, we generate a prior mask from this information and provide coarse spatial location about the target for the model through a simple prior-guided attention module (PGA). Experiments on three different datasets validate that our proposed approach outperforms existing methods.
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References
Wang, G., et al.: DeepiGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1559–1572 (2018). Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)
Zaidi, H., El Naqa, I.: PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur. J. Nucl. Med. Mol. Imaging 37, 2165–2187 (2010)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part III. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al.: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
Roy, A.G., Siddiqui, S., Pölsterl, S., et al.: ‘Squeeze & excite’ guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)
Ouyang, C., Biffi, C., Chen, C., et al.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision–ECCV 2020, Part XXIX, pp. 762–780. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_45
Hansen, S., Gautam, S., Jenssen, R., et al.: Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Med. Image Anal. 78, 102385 (2022)
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Iqbal, E., Safarov, S., Bang, S.: MSANet: multi-similarity and attention guidance for boosting few-shot segmentation. arXiv preprint arXiv:2206.09667 (2022)
Feng, Y., Wang, Y., Li, H., et al.: Learning what and where to segment: a new perspective on medical image few-shot segmentation. Med. Image Anal. 87, 102834 (2023)
Sung, F., Yang, Y., Zhang, L., et al.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, PMLR, pp. 1126–1135 (2017)
Jamal, M.A., Qi, G.J.: Task agnostic meta-learning for few-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11719–11727 (2019)
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations (2016)
Chen, Z., Fu, Y., Wang, Y.X., et al.: Image deformation meta-networks for one-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8680–8689 (2019)
Chen, Z., Fu, Y., Chen, K., et al.: Image block augmentation for one-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 3379–3386 (2019)
Zhao, A., Balakrishnan, G., Durand, F., et al.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019)
Wang, K., Liew, J.H., Zou, Y., et al.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9197–9206 (2019)
Sun, L., Li, C., Ding, X., et al.: Few-shot medical image segmentation using a global correlation network with discriminative embedding. Comput. Biol. Med. 140, 105067 (2022)
Feng, R., Zheng, X., Gao, T., et al.: Interactive few-shot learning: limited supervision, better medical image segmentation. IEEE Trans. Med. Imaging 40(10), 2575–2588 (2021)
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.) ECCV 2022. LNCS, vol. 13680, pp. 417–434. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20044-1_24
Shen, Q., Li, Y., Jin, J., et al.: Q-net: query-informed few-shot medical image segmentation. arXiv preprint arXiv:2208.11451 (2022)
Tang, H., Liu, X., Sun, S., et al.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3918–3928 (2021)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kavur, A.E., Gezer, N.S., Barış, M., Aslan, S., Conze, P.-H., et al.: CHAOS challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)
Bennett, L., Xu, Z., Eugenio, I.J., Martin, S., Robin, L.T., Arno, K.: MICCAI multi-atlas labeling beyond the cranial vault–workshop and challenge (2015)
Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 581–588. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_67
Acknowledgement
This work was supported in part by STI 2030—Major Projects, under Grant 2021ZD0200403, and partly supported by grants from the National Science Foundation of China, Nos. 62333018, 62372255, U22A2039, 62073231, and 62372318, and supported by the Key Project of Science and Technology of Guangxi (Grant no. 2021AB20147), Guangxi Natural Science Foundation (Grant nos. 2022JJD170019 & 2021JJA170204 & 2021JJA170199) and Guangxi Science and Technology Base and Talents Special Project (Grant nos. 2021AC19354 & 2021AC19394) and by Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy Sciences, and supported by Key Research and Development (Digital Twin) Program of Ningbo City under Grant No. 2023Z219, 2023Z226, and supported by the China Postdoctoral Science Foundation under Grant No. 2023M733400 (Corresponding author: De-Shuang Huang.)
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Teng, P., Cheng, Y., Wang, X., Pan, YJ., Yuan, C. (2024). Self-Guided Local Prototype Network for Few-Shot Medical Image Segmentation. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_3
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DOI: https://doi.org/10.1007/978-981-97-0903-8_3
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