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

• DOI: 10.3389/fcvm.2022.804442
• Publication Date: 2022-02-25T06:27:38Z
• Authors: Yutian Chen; Wen Xie; Jiawei Zhang; Hailong Qiu; Dewen Zeng; Yiyu Shi; Haiyun Yuan; Jian Zhuang; Qianjun Jia; Yanchun Zhang; Yuhao Dong; Meiping Huang; Xiaowei Xu
• Keywords: FOS: Computer and information sciences; temporal consistency; diagnosis; Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Computer Science - Computer Vision and Pattern Recognition; Electrical Engineering and Systems Science - Image and Video Processing; Cardiovascular Medicine; cardiac sequences; 03 medical and health sciences; 0302 clinical medicine; RC666-701; FOS: Electrical engineering, electronic engineering, information engineering; Diseases of the circulatory (Cardiovascular) system; Cardiology and Cardiovascular Medicine; myocardial segmentation; coronary artery disease; MRI

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.