A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging
Joint Authors
Yang, Fan
Zhang, Yan
Lei, Pinggui
Wang, Lihui
Miao, Yuehong
Xie, Hong
Zeng, Zhu
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-30
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Objectives.
The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging.
Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis.
Method.
We proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting.
Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing.
Results.
The average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV).
The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV).
Conclusions.
The proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.
American Psychological Association (APA)
Yang, Fan& Zhang, Yan& Lei, Pinggui& Wang, Lihui& Miao, Yuehong& Xie, Hong…[et al.]. 2019. A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging. BioMed Research International،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1126121
Modern Language Association (MLA)
Yang, Fan…[et al.]. A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging. BioMed Research International No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1126121
American Medical Association (AMA)
Yang, Fan& Zhang, Yan& Lei, Pinggui& Wang, Lihui& Miao, Yuehong& Xie, Hong…[et al.]. A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1126121
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126121