YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 11 2019
01 11 2019
Historique:
received:
16
01
2019
revised:
22
03
2019
accepted:
07
05
2019
pubmed:
17
5
2019
medline:
2
7
2020
entrez:
17
5
2019
Statut:
ppublish
Résumé
We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 31095270
pii: 5490207
doi: 10.1093/bioinformatics/btz402
pmc: PMC6821424
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4525-4527Informations de copyright
© The Author(s) 2019. Published by Oxford University Press.
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