Point Transformer For Coronary Artery Labeling
This work addresses the need for accurate and automatic vessel labeling to improve diagnostic efficiency and reduce manual effort for clinicians in coronary artery disease diagnosis, but it appears incremental as it adapts an existing transformer approach to a specific medical imaging task.
The authors tackled the problem of automatic vessel labeling in coronary CT angiography scans by proposing a Point Transformer-based method that uses only coronary artery segmentation, achieving efficacy as demonstrated on 53 subjects.
Coronary CT angiography (CCTA) scans are widely used for diagnosis of coronary artery diseases. An accurate and automatic vessel labeling algorithm for CCTA analysis can significantly improve the diagnostic efficiency and reduce the clinicians'manual efforts. In this paper, we propose a simple vessel labeling method based on the Point Transformer, which only needs the coronary artery segmentation. Specifically, firstly, the coronary segmentation is transformed to point cloud. Then, these points are fed into the hierarchical transformer blocks to obtain the multi-level features, including local and global features. Finally, the network output the semantic classification points and map them to centerline labeling. This method is only based on the structure of coronary segmentation and need not other features, so it is easy to generalize to other vessel labeling tasks, e.g., head and neck vessel labeling. To evaluate the performance of our proposed method, CCTA scans of 53 subjects are collected in our experiment. The experimental results demonstrate the efficacy of this approach.