CAC-EMVT: Efficient Coronary Artery Calcium Segmentation with Multi-scale Vision Transformers

In clinical practice, as a powerful and independent risk indicator of cardiovascular disease (CVD), accurate coronary artery calcium (CAC) segmentation can provide important information for the early diagnosis of CVD. However, due to the small and inconsistent CAC usually has fuzzy boundaries, which leads existing segmentation methods to suffer from unsatisfactory performance. To tackle this challenge, we propose a novel Efficient Multi-scale Vision Transformers for CAC segmentation (CAC-EMVT), which uses both the local and global branches to jointly model short- and long-range dependencies. CAC-EMVT is mainly composed of three modules: 1) a key factor sampling (KFS) module, which is used to mine the key factors of the image to perform low-rank reconstruction of highly structured features; 2) a non-local sparse context fusion (NSCF) module, which is used to efficiently model the global context information of shallow texture features; and 3) a non-local multi-scale context aggregation (NMCA) module, which can be applied to cross-level features to collect long-range dependencies from multiple scales. Undeniably, the newly proposed decomposable positional encoding plays a vital role in the performance improvement of the above modules. Extensive experiments are conducted on the CT scans of 130 CVD patients under 4-fold cross-validation and have demonstrated our CAC-EMVT notably outperforms the state-of-the-art methods in terms of both the mean Dice similarity coefficient (mDice) of 75.39%± 3.17 and mean surface distance (MSD) of 1.93%± 0.46. This reveals the effectiveness and the potential of our model in the clinical setting.