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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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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