YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Alex X Lu; Taraneh Zarin; Ian S Hsu; Alan M Moses

doi:10.1093/bioinformatics/btz402

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Bioinformatics. 2019 Nov 1;35(21):4525-4527. doi: 10.1093/bioinformatics/btz402.

Authors

Alex X Lu¹, Taraneh Zarin², Ian S Hsu², Alan M Moses^{1

2

3}

Affiliations

¹ Department of Computer Science, University of Toronto, Toronto, ON, Canada.
² Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada.
³ Center for Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada.

Abstract

Summary: 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.

Availability and implementation: YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cell Count
Microscopy*
Saccharomyces cerevisiae
Software*