Motivation: This paper aims to mitigate the labor-intensive, time-consuming processes and interobserver variability that currently limit diagnostic efficiency in managing atherosclerotic diseases. Goal(s): To develop a deep learning-enhanced architecture for automated segmentation of extracranial carotid artery and an intelligent quantitative diagnosis of the degree of stenosis, in comparison with DSA. Approach: This two-stage architecture comprises modules for artery localization, automatic segmentation, and quantitative stenosis evaluation. It localizes extracranial carotid arteries within an ROI, subsequently segmenting and classifying stenosis from 3D reconstructions. Results: The model achieved DSC of 0.9737 and AUC of 0.89, validating its effectiveness in enhancing segmentation performance and diagnostic efficiency. Impact: This pipeline demonstrates high concordance with DSA and could significantly enhance cardiovascular risk assessment and atherosclerotic disease diagnosis in a non-invasive, radiation-free manner. Its clinical implementation may streamline diagnostic workflows and aid in the management of carotid artery disease.
Zheng et al. (Tue,) studied this question.