Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Hesamian, MH; Jia, W; He, X; Kennedy, P

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Hesamian, MH Jia, W

He, X

Kennedy, P

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Publication Type:: Journal Article
Citation:: Journal of Digital Imaging, 2019, 32 (4), pp. 582 - 596
Issue Date:: 2019-08-15

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Field	Value	Language
dc.contributor.author	Hesamian, MH	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Kennedy, P https://orcid.org/0000-0001-7837-3171	en_US
dc.date.issued	2019-08-15	en_US
dc.identifier.citation	Journal of Digital Imaging, 2019, 32 (4), pp. 582 - 596	en_US
dc.identifier.issn	0897-1889	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134394
dc.description.abstract	© 2019, The Author(s). Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.	en_US
dc.relation.ispartof	Journal of Digital Imaging	en_US
dc.relation.isbasedon	10.1007/s10278-019-00227-x	en_US
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Nuclear Medicine & Medical Imaging	en_US
dc.title	Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	32	en_US
utslib.for	1103 Clinical Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	in_progress	*
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

© 2019, The Author(s). Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134394