Positive-unlabeled learning for coronary artery segmentation in CCTA images

Abstract

Accurate three-dimensional (3D) segmentation of the coronary artery is an essential step in the quantitative analysis of the coronary arteries. However, due to the small size and complex morphology of the coronary arteries, voxel-by-voxel labeling of the complete coronary artery in 3D computed coronary tomography angiography images is both difficult and laborious. To alleviate the workload of annotating, it is possible to randomly label only a fraction of the positive samples and leave all remaining instances unlabeled, known as the positive-unlabeled (PU) learning problem. Due to the presence of coronary artery-like structures and the absence of negative annotations, we propose a novel sample-selection-based PU learning method for coronary artery segmentation. Specifically, only pseudo-negative labels (PNLs) are generated during the self-training process, and all data are further exploited implicitly using the teacher–student (TS) framework. To address the difficulty of detecting tiny coronary artery branches, we propose a post-processing method by exploiting the variance of multi-scale features in the inference stage. Extensive experiments were conducted on a self-constructed dataset and the publicly available ASOCA dataset. The results demonstrate that our proposed method performs better than baseline supervised and state-of-the-art PU learning methods. Notably, even in extreme cases where more than 80% of annotations are missing, our method still achieves significant gains. When the proportion of missing annotations is relatively low, our method even outperforms the backbone trained with ground truth annotations.