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Coronary artery segmentation on coronary-computed tomography angiography(CCTA) images is crucial for clinical use. Due to the expertise-required andlabor-intensive annotation process, there is a growing demand for the relevantlabel-efficient learning algorithms. To this end, we propose partial vesselsannotation (PVA) based on the challenges of coronary artery segmentation andclinical diagnostic characteristics. Further, we propose a progressive weaklysupervised learning framework to achieve accurate segmentation under PVA.First, our proposed framework learns the local features of vessels to propagatethe knowledge to unlabeled regions. Subsequently, it learns the globalstructure by utilizing the propagated knowledge, and corrects the errorsintroduced in the propagation process. Finally, it leverages the similaritybetween feature embeddings and the feature prototype to enhance testingoutputs. Experiments on clinical data reveals that our proposed frameworkoutperforms the competing methods under PVA (24.29% vessels), and achievescomparable performance in trunk continuity with the baseline model using fullannotation (100% vessels).

Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning