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

Yuyin Zhou, Zhe Li, Song Bai, Chong Wang, Xinlei Chen, Mei Han, Alan Yuille

Abstract:

Accurate multi-organ abdominal CT segmentation is essential to many clinical applications such as computeraided intervention. As data annotation requires massive
human labor from experienced radiologists, it is common that training data are partially labeled, e.g., pancreas
datasets only have the pancreas labeled while leaving the
rest marked as background. 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, prior statistics obtained from the fully-labeled
dataset. As our training objective is difficult to be directly
optimized using stochastic gradient descent, we propose to
reformulate it in a min-max form and optimize it 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%.