Automatic ventricular nuclear magnetic resonance image processing with deep learning

Yong, B; Wang, C; Shen, J; Li, F; Yin, H; Zhou, R

Automatic ventricular nuclear magnetic resonance image processing with deep learning

Yong, B Wang, C Shen, J

Li, F Yin, H Zhou, R

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Yong, B
dc.contributor.author	Wang, C
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140
dc.contributor.author	Li, F
dc.contributor.author	Yin, H
dc.contributor.author	Zhou, R
dc.date.accessioned	2020-11-01T20:28:28Z
dc.date.available	2020-11-01T20:28:28Z
dc.date.issued	2020-01-01
dc.identifier.citation	Multimedia Tools and Applications, 2020
dc.identifier.issn	1380-7501
dc.identifier.issn	1573-7721
dc.identifier.uri	http://hdl.handle.net/10453/143672
dc.description.abstract	© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they have caused widespread concern in recent years. At present, the diagnosis of CVD is mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic resonance (NMR) technologies. NMR imaging technology is widely used in medical applications owing to its characteristics of high resolution and very low radiation. However, manual NMR image segmentation is time-consuming and error-prone, which has led to the research on automatic NMR image segmentation technologies. Researchers tend to explore the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In this study, based on deep learning technology, we propose a layered Mask R-CNN segmentation method to segment ventricular NMR images. The experimental results show that the mean dice metrics (DM) of left ventricular segmentation and right ventricular segmentation are 0.92 and 0.89, and the Hausdorff distance (HD) metrics are 4.78 mm and 7.03 mm. Our research indicates that the proposed novel method has great potential to automate the ventricular NMR image segmentation. We also discuss the automatic abnormal ventricular systolic function detection method based on the proposed layered segmentation model.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Multimedia Tools and Applications
dc.relation.isbasedon	10.1007/s11042-020-08911-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems, 0801 Artificial Intelligence and Image Processing
dc.subject.classification	Software Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Automatic ventricular nuclear magnetic resonance image processing with deep learning
dc.type	Journal Article
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-11-01T20:28:20Z
pubs.publication-status	Published

Abstract:

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they have caused widespread concern in recent years. At present, the diagnosis of CVD is mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic resonance (NMR) technologies. NMR imaging technology is widely used in medical applications owing to its characteristics of high resolution and very low radiation. However, manual NMR image segmentation is time-consuming and error-prone, which has led to the research on automatic NMR image segmentation technologies. Researchers tend to explore the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In this study, based on deep learning technology, we propose a layered Mask R-CNN segmentation method to segment ventricular NMR images. The experimental results show that the mean dice metrics (DM) of left ventricular segmentation and right ventricular segmentation are 0.92 and 0.89, and the Hausdorff distance (HD) metrics are 4.78 mm and 7.03 mm. Our research indicates that the proposed novel method has great potential to automate the ventricular NMR image segmentation. We also discuss the automatic abnormal ventricular systolic function detection method based on the proposed layered segmentation model.

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http://hdl.handle.net/10453/143672