A Gated nmODE Network for Coronary Artery Segmentation in Computed Tomography Angiography Images

Precise and efficient diagnosis of coronary artery disease (CAD) greatly depend on the automated segmentation of coronary arteries. However, due to the presence of noise in Computed Tomography Angiography (CTA) images and the complex shape of coronary arteries, which are easily interfered by surrounding tissues (such as myocardium, lung tissue, etc.), segmenting coronary arteries from low-contrast background images is a challenging task. To capture the diverse morphology and feature correlations of coronary arteries, we build upon an encoder-decoder architecture and leverage the dynamic modeling capabilities of Neural Memory Ordinary Differential Equations (nmODE). We introduce the gating mechanism to modulate weights, thereby constructing the Gate-nmODE module. This module effectively enhances stability through feature weight adjustment, correlation modeling, dynamic balancing, and other mechanisms. Within the experimental section, we conduct extensive trials on a dataset collected on real clinical datasets, demonstrating our approach achieves good performances compared other state-of-the-art method.