YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells-Reference-Cited by-同舟云学术

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

Published:2019-05-16 Issue:21 Volume:35 Page:4525-4527
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Lu Alex X¹^ORCID,Zarin Taraneh²,Hsu Ian S²,Moses Alan M¹²³

Affiliation:

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

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.

Funder

National Science and Engineering Research Council

Canada Research Chairs

Canadian Foundation for Innovation

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz402/28756709/btz402.pdf

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