Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Frontiers in Cardiovascular Medicine

Yanchun Zhang¹  Jiawei Zhang¹  Meiping Huang²  Yuhao Dong²  Qianjun Jia²  Dewen Zeng³  Yiyu Shi³  Yutian Chen⁴  Wen Xie⁵  Xiaowei Xu⁵  Haiyun Yuan⁵  Jian Zhuang⁵  Hailong Qiu⁵ 

[1] Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China;Department of Catheterization Lab, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, United States;Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States;Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangzhou, China;Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China;Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China;

关键词: myocardial segmentation;    MRI;    cardiac sequences;    temporal consistency;    coronary artery disease;    diagnosis;   

DOI  :  10.3389/fcvm.2022.804442

来源: DOAJ

【 摘 要 】

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

【 授权许可】

Unknown