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 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, a prior statistics
obtained from the fully-labeled dataset. As our training objective is difficult
to be directly optimized using stochastic gradient descent [20], 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%.

本文提出了先验知识神经网络 (PaNN) 方法来解决多器官 CT 分割中部分标记数据集的背景模糊问题，PaNN 应用了解剖先验知识指导训练。在 MICCAI2015 挑战中，PaNN 模型取得了 84.97% 的平均 Dice 分数，超过之前的最优结果 3.27%。