Abstract
Partially Supervised Multi-Organ Segmentation (PSMOS) has attracted increasing attention. However, facing with challenges from lacking sufficiently labeled data and cross-site data discrepancy, PSMOS remains largely an unsolved problem. In this paper, to fully take advantage of the unlabeled data, we propose to incorporate voxel-to-organ affinity in embedding space into a consistency learning framework, ensuring consistency in both label space and latent feature space. Furthermore, to mitigate the cross-site data discrepancy, we propose to propagate the organ-specific feature centers and inter-organ affinity relationships across different sites, calibrating the multi-site feature distribution from a statistical perspective. Extensive experiments manifest that our method generates favorable results compared with other state-of-the-art methods, especially on hard organs with relatively smaller sizes.
This study was partially supported by the National Natural Science Foundation of China via projects U20A20199 and 62201341.
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References
Bennett, L., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge. In: Proceedings of MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge. vol. 5, pp. 12 (2015)
Nicholas, H., et al.: The kits19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes. arXiv preprint arXiv:1904.00445 (2019)
Amber L.S., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)
Konstantin, D., Kaufman, A.E.: Learning multi-class segmentations from single-class datasets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9501–9511 (2019)
Zhang, J., Xie, Y., Xia, Y., Shen, C.: DoDNet: learning to segment multi-organ and tumors from multiple partially labeled datasets. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1195–1204 (2021)
Huang, R., Zheng, Y., Hu, Z., Zhang, S., Li, H.: Multi-organ segmentation via co-training weight-averaged models from few-organ datasets. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 146–155. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59719-1_15
Xi, F., Yan, P.: Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans. Med. Imaging 39(11), 3619–3629 (2020)
Zhou, Y., et al.: Prior-aware neural network for partially-supervised multi-organ segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10672–10681 (2019)
Shi, G., Xiao, L., Chen, Y., Zhou, S.K.: Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Med. Image Anal. 70, 101979 (2021)
Jisoo, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. In: Advances in Neural Information Processing Systems. vol. 32 (2019)
Abuduweili, A., Li, X., Shi, H., Xu, C.Z., Dou, D.: Adaptive consistency regularization for semi-supervised transfer learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6923–6932 (2021)
Ouali, Y., Hudelot, C., Tami, M. Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12674–12684 (2020)
Samuli, L., Aila, T.: Temporal ensembling for semi-supervised learning. In: International Conference on Learning Representations (ICLR) (2017)
Antti, T., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems. vol. 30 (2017)
French, G., Laine, S., Aila, T., Mackiewicz, M., Finlayson, G.: Semi-supervised semantic segmentation needs strong, varied perturbations. In: British Machine Vision Conference (2020)
Zheng, K., Xu, J., Wei, J.: Double noise mean teacher self-Ensembling model for semi-supervised tumor segmentation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1446–1450 (2022)
Bento, M., Fantini, I., Park, J., Rittner, L., Frayne, R.: Deep learning in large and multi-site structural brain MR imaging datasets. Front. Neuroinformatics 15(82), 805669 (2022)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., Ronneberger, O.: 3D-UNet: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI, pp. 424–432 (2016)
Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723–773 (2012)
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Zhou, Q., Liu, P., Zheng, G. (2023). Partially Supervised Multi-organ Segmentation via Affinity-Aware Consistency Learning and Cross Site Feature Alignment. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_63
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