Segmentation and Vascular Vectorization for Coronary Artery by Geometry-Based Cascaded Neural Network.

Area of Science:

• Medical Imaging
• Deep Learning
• Cardiovascular Imaging

Background:

• Coronary artery segmentation in CCTA is crucial for quantitative analysis.
• Deep learning methods face challenges due to complex coronary artery structures, low resolution, and poor contrast, leading to segmentation fragmentation.
• Existing segmentation methods struggle with intricate vessel branches and accurate representation.

Purpose of the Study:

• To propose a geometry-based cascaded segmentation method for accurate coronary artery segmentation in CCTA.
• To overcome limitations of existing methods, particularly fragmentation and inaccurate representation of complex structures.
• To introduce a novel mesh annotation strategy for improved geometric segmentation.

Main Methods:

• A cascaded network integrating geometric deformation networks for coronary artery segmentation and vectorization.
• A novel mesh reconstruction method using regularized morphology for finer vectorized annotations.
• Development and utilization of the CCA-200 dataset with radiologist-annotated coronary artery diameters.

Main Results:

• The proposed method achieved a Dice score of 0.778 on the CCA-200 dataset and 0.895 on the ASOCA dataset.
• The geometry-based model generated accurate, intact, and smooth coronary artery segmentations, free from fragmentation.
• The novel mesh annotation strategy effectively avoided bifurcation adhesion and point cloud dispersion in intricate branches.

Conclusions:

• The geometry-based cascaded segmentation method offers a significant advancement in coronary artery analysis from CCTA images.
• The approach successfully addresses the challenges of complex anatomy and image quality limitations, producing superior segmentation results.
• The developed CCA-200 dataset and novel annotation method contribute to the field of cardiovascular image analysis.