Abstract #4514

Deep Learning Based Key Point Detection Algorithm to Assist Accurate Vascular Centerline Extraction and Automatic Quantitative Plaque Analysis

Xiqian Zhang^1,2, Wanqing Sun³, Xiong Yang³, Yufei Mao³, Ye Li^1,2,4,5, Dong Liang^1,2,4,5, Xin Liu^1,2,4,5, Hairong Zheng^1,2,4,5, and Na Zhang^1,2,4,5

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, ²University of Chinese Academy of Sciences, Beijing, China, ³Department of Image Advanced Analysis of HSW BU, Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China, ⁴Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen, China, ⁵United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Plaque analysis;Vascular centerline extraction

Motivation: Magnetic Resonance Vessel Wall Imaging is a crucial method for assessing arterial plaques.

Goal(s): Achieving a rapid and accurate algorithm for vascular centerline extraction and automatic detection of the pituitary stalk, thereby enabling fast, automatic, and quantitative analysis of plaques.

Approach: Proposed a point detection algorithm based on V-NET, designed to achieve rapid and accurate extraction of vascular centerlines and automatic localization of the pituitary stalk.

Results: Accurate key point detection had been achieved, enhancing the precision of vascular centerline extraction and enabling fast and accurate automatic localization of the pituitary stalk.

Impact: The accuracy of vascular centerline detection has been improved, and rapid and automatic quantitative analysis of plaque enhancement has been realized. This can assist in enhancing diagnostic efficiency in clinical settings.

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