Semi-Supervised Learning and Focal Masking for Vessel Segmentation in X-Ray Coronary Angiography

Coronary artery disease (CAD) is a major cause of mortality, with diagnosis largely dependent on quantitative coronary analysis (QCA) using coronary angiogram (CAG) images. Advances in deep learning have significantly improved medical image processing, creating new possibilities for automated QCA. This paper focuses on applying machine learning techniques to vessel segmentation in CAG images, a crucial first step in QCA. We propose a novel semi-supervised learning approach, introducing a semi-supervised Dice loss with three components: a foreground Dice loss for accurate segmentation, a background Dice loss to reduce false positives, and a semi-supervised Dice loss to generalize to unlabeled areas. We also present a focal masking strategy that adaptively targets underperforming regions, such as minor vessels, ensuring the model focuses on challenging areas to improve overall segmentation performance. To our knowledge, this is the first application of semi-supervised learning for CAG vessel segmentation using deep learning, offering a more efficient and accurate solution that could enhance diagnostic outcomes in clinical settings.