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).

翻译：冠状动脉计算机断层扫描血管造影（CCTA）图像中的冠状动脉分割对临床诊断至关重要。由于标注过程对专业知识要求高且耗时费力，相关标签高效学习算法的需求日益增长。为此，我们针对冠状动脉分割的挑战及临床诊断特征，提出部分血管标注（PVA）策略。进一步，我们提出一种渐进式弱监督学习框架，在PVA条件下实现精确分割。该框架首先学习血管的局部特征，将知识传播至未标注区域；随后利用传播的知识学习全局结构，并修正传播过程中引入的误差；最后通过特征嵌入与特征原型之间的相似性增强测试输出。临床数据实验表明，在PVA条件下（24.29%血管标注），本框架性能优于对照方法，且在主干连续性方面达到与全标注（100%血管）基线模型相当的水平。