Coronary artery segmentation framework based on three types of U-Net and voting ensembles

Coronary artery (CA) segmentation is critical for enabling disease diagnosis. However, the structural complexity and extensive branching of CAs may cause the segmentation outcomes of existing methods to exhibit discontinuities and considerable pseudo-CA regions. Therefore, we propose a voting-based ensemble segmentation framework based on three U-Net types to capture CA structural features from global and local perspectives. The lightweight U-Net performs direct segmentation on CAs, helping to eliminate interferences from small connected regions during segmentation and preserve global information. Patch-based and multi-slice U-Nets provide superior local partition information. Finally, a voting-based strategy is adopted to ensemble the segmentation results for the three models to obtain the final result. Our proposed segmentation framework performs well, attaining a Dice score of 82.31% on a large dataset.