Multi-scale across attention incorporated network for X-ray coronary vessel segmentation

Abstract

Most attention-embedded networks fall short in effectively integrating spatial / channel-wise information across diverse scales, leading to suboptimal performance for coronary vessels segmentation in X-ray digital subtraction angiography images. To address this limitation, a multi-scale across attention incorporated network (named MS2A-Net) is introduced. MS2A-Net accepts original and enhanced images as inputs, leveraging complementary information provided by the different contrasts within the images. Furthermore, MS2A is designed to integrate features across multiple levels, scales and sources, for effectively extracting deep semantic information. After the incorporation of features with adaptive weightings, segmentation accuracy is refined. Qualitative and quantitative experiment results prove that MS2A-Net not only outperforms state-of-the-art tactics but also shows superior performance, e.g., higher intersection over union, Dice coefficient, broader areas under receiver operating characteristic curves.