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Benchmark for multi-cellular segmentation of bright field microscopy images

BACKGROUND: Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and compari...

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Autores principales: Zaritsky, Assaf, Manor, Nathan, Wolf, Lior, Ben-Jacob, Eshel, Tsarfaty, Ilan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3826518/
https://www.ncbi.nlm.nih.gov/pubmed/24195722
http://dx.doi.org/10.1186/1471-2105-14-319
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author Zaritsky, Assaf
Manor, Nathan
Wolf, Lior
Ben-Jacob, Eshel
Tsarfaty, Ilan
author_facet Zaritsky, Assaf
Manor, Nathan
Wolf, Lior
Ben-Jacob, Eshel
Tsarfaty, Ilan
author_sort Zaritsky, Assaf
collection PubMed
description BACKGROUND: Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives. DESCRIPTION: A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools. CONCLUSIONS: The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets.
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spelling pubmed-38265182013-11-18 Benchmark for multi-cellular segmentation of bright field microscopy images Zaritsky, Assaf Manor, Nathan Wolf, Lior Ben-Jacob, Eshel Tsarfaty, Ilan BMC Bioinformatics Database BACKGROUND: Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives. DESCRIPTION: A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools. CONCLUSIONS: The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets. BioMed Central 2013-11-07 /pmc/articles/PMC3826518/ /pubmed/24195722 http://dx.doi.org/10.1186/1471-2105-14-319 Text en Copyright © 2013 Zaritsky et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database
Zaritsky, Assaf
Manor, Nathan
Wolf, Lior
Ben-Jacob, Eshel
Tsarfaty, Ilan
Benchmark for multi-cellular segmentation of bright field microscopy images
title Benchmark for multi-cellular segmentation of bright field microscopy images
title_full Benchmark for multi-cellular segmentation of bright field microscopy images
title_fullStr Benchmark for multi-cellular segmentation of bright field microscopy images
title_full_unstemmed Benchmark for multi-cellular segmentation of bright field microscopy images
title_short Benchmark for multi-cellular segmentation of bright field microscopy images
title_sort benchmark for multi-cellular segmentation of bright field microscopy images
topic Database
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3826518/
https://www.ncbi.nlm.nih.gov/pubmed/24195722
http://dx.doi.org/10.1186/1471-2105-14-319
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