ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors created a large, detailed dataset to help improve automatic methods for outlining the inside of heart blood vessels in CT scans. This is important for measuring problems like plaque buildup and fat around the vessels. They compared existing methods to see how well they work across different conditions and provided data that shows results in ways useful to doctors. Their dataset can be used to make better tools for studying heart vessel health and blood flow.
coronary vessel lumensegmentationcoronary computed tomography angiography (CCTA)atherosclerotic plaqueperivascular adipose tissuevoxel-wise annotationscenterlinesmesh surfacesinter-observer variabilityhaemodynamic modelling
Authors
Kit M. Bransby, Esther Øksnebjerg, Kristoffer Kjær, Jacob Kirkeby, Yasmin El Youssef, Aïda Jiménez, Philip R. Pedersson, Martina C. de Knegt, Klaus F. Kofoed, Rasmus R. Paulsen
Abstract
Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose tissue in coronary computed tomography angiography (CCTA). Cardiologists rely on semi-automated methods for this task because manual vessel tracing and segmentation are labour-intensive. Although many automated methods have been proposed, their validation remains limited by the lack of large, high-quality publicly available datasets. We provide a new dataset of voxel-wise annotations of the vessel lumen and coronary segments, alongside centerlines, and mesh surfaces for 800 scans from the publicly available ImageCAS dataset. Using this dataset, we benchmark established lumen segmentation methods against inter-observer variability, stratifying performance by disease, image quality, coronary dominance, coronary segment, vessel diameter, and lumen attenuation. These labels allow segmentation accuracy to be described in anatomical and clinical context rather than reported as a single aggregate score. The dataset supports the development and validation of methods for lumen segmentation, plaque and perivascular quantification, and haemodynamic modelling.