Coronary artery segmentation for automatic stenosis detection – UROP Spring Symposium 2021

Coronary artery segmentation for automatic stenosis detection

Xinyu Cui

Xinyu Cui

Pronouns: She/Her/Hers

Research Mentor(s): Kayvan Najarian, Professor
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 19
Presenter: 4

Event Link


One type of coronary artery disease (CAD), stenosis, is characterized by the narrowing of the coronary arteries, which is the leading cause of death in the United States. One of the most popular ways to diagnose stenosis is the coronary angiogram, from which a doctor can diagnose stenosis by finding places of narrowing in the arteries with the video or the pictures acquired. A lot of studies are currently focusing on automatic coronary artery segmentation, a critical step of a computer-aided system that assists doctors in detecting coronary stenosis. Here we propose a deep learning pipeline using DenseNet-backbone U-Net for coronary artery segmentation in angiogram images, which could be combined with pre-processing and post-processing steps for stenosis detection.

Authors: Xinyu Cui, Lu Wang
Research Method: Computer Programming

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