Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis
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 tech...
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Published in | Frontiers in cardiovascular medicine Vol. 9; p. 804442 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
25.02.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Cardiovascular Imaging, a section of the journal Frontiers in Cardiovascular Medicine These authors have contributed equally to this work Reviewed by: Joris Fournel, UMR7373 Institut de Mathématiques de Marseille (I2M), France; Musa Abdulkareem, Barts Health NHS Trust, United Kingdom Edited by: Nay Aung, Queen Mary University of London, United Kingdom |
ISSN: | 2297-055X 2297-055X |
DOI: | 10.3389/fcvm.2022.804442 |