GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.

Wang, Y; Wang, W; Liu, D; Hou, W; Zhou, T; Ji, Z

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.

Wang, Y Wang, W Liu, D Hou, W Zhou, T Ji, Z

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Genome Biol, 2023, 24, (1), pp. 235
Issue Date:: 2023-10-19

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Wang, W
dc.contributor.author	Liu, D
dc.contributor.author	Hou, W
dc.contributor.author	Zhou, T
dc.contributor.author	Ji, Z
dc.date.accessioned	2024-05-04T02:07:41Z
dc.date.available	2023-09-08
dc.date.available	2024-05-04T02:07:41Z
dc.date.issued	2023-10-19
dc.identifier.citation	Genome Biol, 2023, 24, (1), pp. 235
dc.identifier.issn	1474-7596
dc.identifier.issn	1474-760X
dc.identifier.uri	http://hdl.handle.net/10453/178639
dc.description.abstract	When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.
dc.format	Electronic
dc.language	eng
dc.publisher	Springer Nature
dc.relation.ispartof	Genome Biol
dc.relation.isbasedon	10.1186/s13059-023-03054-0
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	05 Environmental Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.mesh	Deep Learning
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	RNA
dc.subject.mesh	Gene Expression
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	RNA
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	Gene Expression
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Deep Learning
dc.subject.mesh	Deep Learning
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	RNA
dc.subject.mesh	Gene Expression
dc.subject.mesh	Image Processing, Computer-Assisted
dc.title	GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.
dc.type	Journal Article
utslib.citation.volume	24
utslib.location.activity	England
utslib.for	05 Environmental Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-04T02:07:35Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	24
utslib.citation.issue	1

Abstract:

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.

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http://hdl.handle.net/10453/178639