Examinee-Examiner Network: Weakly Supervised Accurate Coronary Lumen Segmentation Using Centerline Constraint.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Disease

Background:

• Accurate coronary lumen segmentation in coronary-computed tomography angiography (CCTA) is vital for stenosis quantification and fractional flow reserve (FFR) calculation.
• Challenges include complex lesion morphologies, thin structures, and limited labeled data, complicating automated segmentation.

Purpose of the Study:

• To propose a novel weakly supervised model, the Examinee-Examiner Network (EE-Net), for robust coronary lumen segmentation.
• To address segmentation fractures caused by stenoses and improve network sensitivity to vessel centerlines.
• To enable accurate segmentation with limited lumen labels using a weakly supervised learning strategy.

Main Methods:

• Developed EE-Net fusing lumen semantic features with centerline topological continuity.
• Introduced a Centerline Gaussian Mask Module to enhance centerline sensitivity.
• Implemented Examinee-Examiner Learning for weakly supervised segmentation with customized priors.
• Utilized a Drop Output Layer to manage class imbalance and dynamic class weighting.

Main Results:

• EE-Net demonstrated superior continuity and generalization in coronary lumen segmentation across two datasets.
• Outperformed widely used Convolutional Neural Networks (CNNs) like 3D-UNet.
• Showcased significant potential for accurate segmentation in patients with coronary artery disease.

Conclusions:

• The proposed EE-Net effectively overcomes challenges in coronary lumen segmentation.
• EE-Net offers a promising solution for automated, accurate segmentation in clinical CCTA analysis.
• The model's weakly supervised approach facilitates application in data-scarce scenarios.