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

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 integrat...

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Autores principales: Wang, Yuxing, Wang, Wenguan, Liu, Dongfang, Hou, Wenpin, Zhou, Tianfei, Ji, Zhicheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585768/
https://www.ncbi.nlm.nih.gov/pubmed/37858204
http://dx.doi.org/10.1186/s13059-023-03054-0
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author Wang, Yuxing
Wang, Wenguan
Liu, Dongfang
Hou, Wenpin
Zhou, Tianfei
Ji, Zhicheng
author_facet Wang, Yuxing
Wang, Wenguan
Liu, Dongfang
Hou, Wenpin
Zhou, Tianfei
Ji, Zhicheng
author_sort Wang, Yuxing
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03054-0.
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spelling pubmed-105857682023-10-20 GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging Wang, Yuxing Wang, Wenguan Liu, Dongfang Hou, Wenpin Zhou, Tianfei Ji, Zhicheng Genome Biol Method 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03054-0. BioMed Central 2023-10-19 /pmc/articles/PMC10585768/ /pubmed/37858204 http://dx.doi.org/10.1186/s13059-023-03054-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Wang, Yuxing
Wang, Wenguan
Liu, Dongfang
Hou, Wenpin
Zhou, Tianfei
Ji, Zhicheng
GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title_full GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title_fullStr GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title_full_unstemmed GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title_short GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging
title_sort genesegnet: a deep learning framework for cell segmentation by integrating gene expression and imaging
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585768/
https://www.ncbi.nlm.nih.gov/pubmed/37858204
http://dx.doi.org/10.1186/s13059-023-03054-0
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