Head-and-Neck Organs Segmentation in CT Based on Spatial Prior and Shape Description

Accurate delineation of organs at risk (OARs) is critical for effective radiotherapy in head and neck cancer, and different deep learning methods have been proposed for this task. Although these methods can effectively segment large organs, they all face challenges in segmenting different small organs with high accuracy, due to large numbers, complex distributions, and diverse shapes of small organs in the head and neck region. To address this challenge, this paper proposes a novel segmentation framework that incorporates spatial distribution information of all organs and shape priors of small organs into deep networks to constrain and enhance small organ segmentation. First, a spatial guidance network (SG-Net) is proposed to generate spatial guidance maps (SGMs) of organs, emphasizing the boundaries of different organs and their spatial positional relationships, thereby providing useful spatial cues to constrain organ segmentation. Second, for small-volume organs, we specifically design a deep shape description module (DSDM) to extract organ-specific shape features from CT images and integrate them into the original deep features to enhance the features' sensitivity to shape constraints. Finally, a regularization term is employed to reduce excessive smoothing in the predicted probability maps of the deep network, preserving the shape details of small organs. With this framework, while the segmentation accuracy of large organs is maintained, small organ segmentation is significantly improved. Experimental results demonstrate its effectiveness for segmentation of small organs, with a significant improvement over state-of-the-art methods.