  Coronary artery segmentation on coronary-computed tomography angiography
(CCTA) images is crucial for clinical use. Due to the expertise-required and
labor-intensive annotation process, there is a growing demand for the relevant
label-efficient learning algorithms. To this end, we propose partial vessels
annotation (PVA) based on the challenges of coronary artery segmentation and
clinical diagnostic characteristics. Further, we propose a progressive weakly
supervised learning framework to achieve accurate segmentation under PVA.
First, our proposed framework learns the local features of vessels to propagate
the knowledge to unlabeled regions. Subsequently, it learns the global
structure by utilizing the propagated knowledge, and corrects the errors
introduced in the propagation process. Finally, it leverages the similarity
between feature embeddings and the feature prototype to enhance testing
outputs. Experiments on clinical data reveals that our proposed framework
outperforms the competing methods under PVA (24.29% vessels), and achieves
comparable performance in trunk continuity with the baseline model using full
annotation (100% vessels).
