Format

Send to

Choose Destination
Bioinformatics. 2019 May 16. pii: btz402. doi: 10.1093/bioinformatics/btz402. [Epub ahead of print]

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

Author information

1
Department of Computer Science, University of Toronto, Toronto, Canada.
2
Department of Cells and Systems Biology, University of Toronto, Toronto, Canada.
3
Center for Analysis of Genome Evolution and Function, University of Toronto, Toronto, 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 figures are available at Bioinformatics online.

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center