Gharleghi RAdikari DEllenberger KOoi S.-YEllis CCHUNG-MING CHENGao RHe YHussain RLee C.-YLi JMa JNie ZOliveira BQi YSkandarani YVilaça J.LWang XYang SSowmya ABeier S.2022-11-162022-11-16202208956111https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126649807&doi=10.1016%2fj.compmedimag.2022.102049&partnerID=40&md5=5d9d7b160a60ba74003e9c08f7fa678ehttps://scholars.lib.ntu.edu.tw/handle/123456789/625449Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications. © 2022 The AuthorsCoronary arteries; Image segmentation; Machine learning[SDGs]SDG3Automation; Benchmarking; Computerized tomography; Diseases; Heart; Large dataset; Machine learning; Noninvasive medical procedures; Automated segmentation; Cardiovascular disease; Causes of death; Coronary angiography; Coronary arteries; Coronary artery disease; Coronary vessel; Images segmentations; Noninvasive methods; Vessel structure; Image segmentation; beta adrenergic receptor blocking agent; iohexol; anatomy; Article; computed tomographic angiography; coronary angiography; coronary artery; coronary artery disease; coronary blood vessel; human; image processing; image segmentation; left coronary artery; machine learning; right coronary artery; algorithm; coronary blood vessel; diagnostic imaging; procedures; x-ray computed tomography; Algorithms; Computed Tomography Angiography; Coronary Angiography; Coronary Artery Disease; Coronary Vessels; Humans; Tomography, X-Ray Computedjournal article10.1016/j.compmedimag.2022.102049353343162-s2.0-85126649807