Graph-based Regional Feature Enhancing for Abdominal Multi-Organ Segmentation in CT

Abstract

Automatic segmentation of abdominal organs in CT is of essential importance for radiation therapy and image-guided surgery. However, the development of such automatic solutions remains challenging due to complicated structures and low tissue contrast in abdominal CT images. To address these issues, we propose a novel deep neural network equipped with an edge detection (ED) module and a graph-based regional feature enhancing (GRFE) module for better organ segmentation, by enhancing the long-range representation power of regional features. Specifically, the proposed ED module learns an edge representation by leveraging both fine-grained and structural information. The edge representation is then fused with the segmentation features to provide constraint guidance for better prediction. Our GRFE module propagates features to capture contextual information via graphic voxel-by-voxel connections. The GRFE module leverages the edge representation to highlight the features of boundaries to build strong contextual dependencies between the features of organs' boundaries and central areas. We evaluate the efficacy of the proposed network on two challenging abdominal multi-organ datasets. Experimental results demonstrate that our network outperforms several state-of-the-art methods. The code is publicly available at https://github.com/zefanyang/organseg_dags.