A multi-backbone cascade and morphology-aware segmentation network for complex morphological X-ray coronary artery...

Area of Science:

• Medical Imaging
• Computer Vision
• Artificial Intelligence

Background:

• X-ray coronary angiography is the gold standard for diagnosing coronary artery disease.
• Existing segmentation methods struggle with complex coronary artery morphology, leading to errors and vessel breakage.

Purpose of the Study:

• To develop an advanced segmentation network, MBCMA-Net, for accurate coronary artery analysis.
• To improve feature extraction and complex structure recognition in coronary angiography.

Main Methods:

• Proposed a multi-backbone cascade and morphology-aware segmentation network (MBCMA-Net).
• Incorporated multi-backbone encoders for enhanced feature extraction.
• Integrated a vascular morphology-aware module for complex structure recognition.
• Utilized a centerline loss function to maintain vascular connectivity.

Main Results:

• MBCMA-Net achieved an IoU of 87.14% and a DSC score of 92.72%.
• Demonstrated superior vascular connectivity with a score of 89.05%.
• Outperformed mainstream segmentation algorithms on public datasets (DCA1, CADICA, ARCADE).

Conclusions:

• MBCMA-Net offers a robust solution for coronary artery segmentation.
• The model can serve as a benchmark for future coronary artery segmentation research.
• Improved segmentation accuracy aids in better diagnosis of coronary artery disease.