YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells
| Autor: | Alan M. Moses, Alex X. Lu, Taraneh Zarin, Ian Shenyen Hsu |
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| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Computer science Cell Count Saccharomyces cerevisiae Biochemistry 03 medical and health sciences 0302 clinical medicine Software Microscopy Code (cryptography) Segmentation Molecular Biology 030304 developmental biology 0303 health sciences business.industry Pattern recognition Applications Notes Yeast Computer Science Applications Computational Mathematics ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Artificial intelligence business Bioimage Informatics 030217 neurology & neurosurgery |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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