Frontiers in Cardiovascular Medicine (Feb 2022)

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

  • Yutian Chen,
  • Yutian Chen,
  • Yutian Chen,
  • Wen Xie,
  • Wen Xie,
  • Jiawei Zhang,
  • Jiawei Zhang,
  • Jiawei Zhang,
  • Jiawei Zhang,
  • Hailong Qiu,
  • Hailong Qiu,
  • Dewen Zeng,
  • Yiyu Shi,
  • Haiyun Yuan,
  • Haiyun Yuan,
  • Jian Zhuang,
  • Jian Zhuang,
  • Qianjun Jia,
  • Qianjun Jia,
  • Yanchun Zhang,
  • Yuhao Dong,
  • Yuhao Dong,
  • Yuhao Dong,
  • Meiping Huang,
  • Meiping Huang,
  • Meiping Huang,
  • Xiaowei Xu,
  • Xiaowei Xu

DOI
https://doi.org/10.3389/fcvm.2022.804442
Journal volume & issue
Vol. 9

Abstract

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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.

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