Progressive Perception Learning for Main Coronary Segmentation in X-Ray Angiography.

Area of Science:

• Medical Imaging
• Computer-Aided Diagnosis
• Cardiovascular Disease Analysis

Background:

• Accurate main coronary segmentation is crucial for diagnosing and treating coronary artery disease using X-ray angiography.
• Existing methods struggle with semantic confusion, low contrast, and boundary ambiguity.
• Traditional and deep learning approaches have limitations in capturing semantic relationships and preserving boundary details.

Purpose of the Study:

• To develop an advanced framework for precise main coronary segmentation from X-ray angiography images.
• To overcome the limitations of current segmentation techniques in handling complex coronary structures and image artifacts.

Main Methods:

• Proposed the progressive perception learning (PPL) framework, incorporating context, interference, and boundary perception modules.
• Context perception module captures semantic dependencies between coronary segments.
• Interference perception module enhances foreground vessels and suppresses background artifacts.
• Boundary perception module extracts boundary details by analyzing foreground and background predictions.

Main Results:

• The PPL framework demonstrated high effectiveness in extensive experiments on 1085 subjects.
• Achieved an overall Dice score exceeding 95%, indicating superior segmentation accuracy.
• Outperformed thirteen state-of-the-art coronary segmentation methods.

Conclusions:

• The PPL framework offers a robust solution for main coronary segmentation.
• It significantly improves accuracy and overcomes limitations of previous methods.
• PPL holds promise for enhancing computer-aided diagnosis and treatment planning in cardiology.