Medical Image Segmentation With Deep Atlas Prior

Organ segmentation from medical images is one of the most important pre-processing steps in computer-aided diagnosis, but it is a challenging task because of limited annotated data, low-contrast and non-homogenous textures. Compared with natural images, organs in the medical images have obvious anatomical prior knowledge (e.g., organ shape and position), which can be used to improve the segmentation accuracy. In this paper, we propose a novel segmentation framework which integrates the medical image anatomical prior through loss into the deep learning models. The proposed prior loss function is based on probabilistic atlas, which is called as deep atlas prior (DAP). It includes prior location and shape information of organs, which are important prior information for accurate organ segmentation. Further, we combine the proposed deep atlas prior loss with the conventional likelihood losses such as Dice loss and focal loss into an adaptive Bayesian loss in a Bayesian framework, which consists of a prior and a likelihood. The adaptive Bayesian loss dynamically adjusts the ratio of the DAP loss and the likelihood loss in the training epoch for better learning. The proposed loss function is universal and can be combined with a wide variety of existing deep segmentation models to further enhance their performance. We verify the significance of our proposed framework with some state-of-the-art models, including fully-supervised and semi-supervised segmentation models on a public dataset (ISBI LiTS 2017 Challenge) for liver segmentation and a private dataset for spleen segmentation.