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Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images
Published on: September 22, 2023
Preoperative Decision-Oriented Decoupled Multi-Scale Feature Pyramid and Global Context Aggregation Network for
Jianhua Lin1, Lin Lin1, Zhenzhen Li1
1Fuqing City Hospital Affiliated to Fujian Medical University, Fuqing, China.
Summary
This study introduces a new AI model for precise coronary artery segmentation in X-ray angiography, improving cardiovascular diagnosis and treatment planning by overcoming common segmentation challenges.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Imaging
Background:
- Accurate preoperative coronary artery delineation is crucial for cardiovascular diagnosis and treatment.
- Challenges include class imbalance, poor bifurcation contrast, and overlapping artifacts, leading to segmentation errors.
Purpose of the Study:
- To develop an advanced AI framework for robust coronary artery segmentation.
- To improve the accuracy and reliability of vessel segmentation in X-ray angiography.
Main Methods:
- A decoupled hybrid architecture separating multi-scale detail recovery and global context aggregation.
- Utilizes a Feature Pyramid Network (FPN) neck for edge preservation and a UPerHead for contextual modeling.
Main Results:
- Achieved Dice, IoU, and Centreline Dice scores of 0.7633, 0.6290, and 0.7827 on the ARCADE benchmark.
- Outperformed baseline methods and demonstrated improved contextual attention and interpretability.
Conclusions:
- The proposed framework enables robust preoperative vessel segmentation.
- Supports better assessment of distal continuity, bifurcation morphology, and lesion-adjacent boundaries for clinical decisions.
