Advanced Segmentation of X-Ray Coronary Angiography: Leveraging DA-TransUNet for Enhanced Vessel Visualization

Description

We used Automatic Region-based coronary artery disease diagnostics using X-Ray angiography imagEs (ARCADE) dataset, publicly available . In this work we applied ARCADE dataset that includes 3,000 XCA images were equally divided for artery classification and stenosis detection, with 1,500 images allocated to each category. The ARCADE dataset was used to diagnose CAD, emphasizing coronary channel tree segmentation with the SYNTAX Score and stenosis detection. For both tasks 1,000 images allocated for training, 200 allotted for validation, and 300 reserved for test.

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