Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation

Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Elliot Fishman, Alan L. Yuille; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 10672-10681


Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computer-aided intervention. As data annotation requires massive human labor from experienced radiologists, it is common that training data is usually partially-labeled. However, these background labels can be misleading in multi-organ segmentation since the "background" usually contains some other organs of interest. To address the background ambiguity in these partially-labeled datasets, we propose Prior-aware Neural Network (PaNN) via explicitly incorporating anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. More specifically, PaNN assumes that the average organ size distributions in the abdomen should approximate their empirical distributions, a prior statistics obtained from the fully-labeled dataset. As our objective is difficult to be directly optimized using stochastic gradient descent, it is reformulated as a min-max form and optimized via the stochastic primal-dual gradient algorithm. PaNN achieves state-of-the-art performance on the MICCAI2015 challenge "Multi-Atlas Labeling Beyond the Cranial Vault", a competition on organ segmentation in the abdomen. We report an average Dice score of 84.97%, surpassing the prior art by a large margin of 3.27%. Code and models will be made publicly available.

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author = {Zhou, Yuyin and Li, Zhe and Bai, Song and Wang, Chong and Chen, Xinlei and Han, Mei and Fishman, Elliot and Yuille, Alan L.},
title = {Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}