International Conference on Computer and Knowledge Engineering

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Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models

Authors :

Fatemeh Fouladi¹ Ali Rostami² Hedieh Sajedi³

1- University of Tehran 2- University of Tehran 3- University of Tehran

Keywords :

mamba،medical image segmentation،selective space models،unet،Coronary Artery Stenosis،semantic segmentation

Abstract :

Coronary artery disease stands as one of the pri- mary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, intrinsic noise in X-ray images, and the fact that stenotic coronary arteries appear narrow and blurred in X-ray angiographies. This study employs five different variants of the Mamba-based model and one variant of the Swin Transformer-based model, primarily based on the U-Net architecture, for the localization of stenosis in Coronary artery disease. Our best results showed an F1 score of 68.79% for the U-Mamba BOT model, representing an 11.8% improvement over the semi-supervised approach.