Mask SAM 3D for coronary artery and plaque segmentation in CCTA images

Coronary artery disease is a major global cause of morbidity and mortality, especially in obstructive CAD patients. Precise segmentation of coronary arteries and atherosclerotic plaques is essential for effective treatment. However, no previous study has addressed the joint segmentation of these two within a unified framework, which motivates our work.

We built a dataset, namely PCCTA120, consisting of 120 CCTA volumes, each annotated with manually delineated masks for coronary arteries and atherosclerotic plaques. We then present Mask SAM 3D, an innovative framework designed for joint segmentation of these two components. In the context of plaque localization within coronary arteries, accurately identifying plaques is a complex task due to the intricate nature of coronary artery and the subtle differences in plaque appearance. To simplify this challenge, we recognized the need for a reliable prior of well-defined artery skeleton and proposed to first generate a precise coronary artery mask with nnUNet. Subsequently, a novel plaque-aware adapter is developed to intensify semantic interactions and refines the accuracy of plaque localization by capitalizing on the prior information embedded within the generated coronary artery mask. Meanwhile, to enhance the model's discriminative ability for accurate joint segmentation, a prototype-guided prediction module that dynamically clusters embedded features into class-specific prototypes is introduced.

Experiments conducted on our self-built dataset show that our method achieves Dice similarity coefficients of 84.5% for artery segmentation and 55.2% for plaque segmentation, outperforming current state-of-the-art methods.

First, we release a new coronary arteries and atherosclerotic plaques segmentation dataset, PCCTA120, to advance the cardiovascular research community. Meanwhile, our framework, Mask SAM 3D, cannot only improve the accuracy of artery segmentation but also enhances that of plaque segmentation. Source code and dataset will be made publicly available.