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

Autor:	Yuxing Wang, Wenguan Wang, Dongfang Liu, Wenpin Hou, Tianfei Zhou, Zhicheng Ji
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Cell segmentation Spatial transcriptomics Deep learning In situ hybridization Biology (General) QH301-705.5 Genetics QH426-470
Zdroj:	Genome Biology, Vol 24, Iss 1, Pp 1-24 (2023)
Druh dokumentu:	article
ISSN:	1474-760X 96437448
DOI:	10.1186/s13059-023-03054-0
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/92e33beeab964fcd9a96437448ba8c77 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ