Abstract:
Coronary artery disease (CAD) remains a leading cause of mortality worldwide. In
severe cases, coronary artery bypass grafting (CABG) as a critical surgical intervention.
Accurate preoperative planning relies heavily on the precise delineation
of coronary arteries from computed tomography angiography (CTA) images, a
time-consuming and variable manual task. This study presents CABGenie, a
deep learning-based system that integrates five state-of-the-art semantic segmentation
models (UNet, VNet, DynUNet, SegResNet, and UNETR). These models
are optimized via an automated hyperparameter tuning using Optuna to perform
coronary artery segmentation from 3D CCTA images in the publicly available
ImageCAS dataset. The system includes a web interface that supports image
upload, segmentation inference, and interactive 3D visualization using ITK/VTK
viewers. Results demonstrate improved model performance after tuning, with
DynUNet achieving the highest Dice Similarity Coefficient of 0.7657. The performance
could still be improved and there is a need for further model refinement to
enhance segmentation accuracy.
