Generative Adversarial Training with Dual-Attention for Vascular Segmentation and Topological Analysis.

Abstract

The vascular topology is of vital importance in building a chemotherapy model for the liver cancer in rats. And segmentation of vessels in the liver is an indispensable part of vessels' topological analysis. In this paper, we proposed and validated a novel pipeline for segmenting liver vessels and extracting their skeletons for topological analysis. We employed a dual-attention based U-Net trained in a generative adversarial network (GAN) fashion to obtain precise segmentations of vessels. For subsequent topological analysis, the vessels' skeletons are extracted and classified according to their lengths and bifurcation orders. Based on 40 samples with carefully-annotated ground truth labels, our experiments revealed consistent superiority in terms of both segmentation accuracy and topology correctness, demonstrating the robustness of the proposed pipeline.