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Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms

BACKGROUND: Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation app...

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Autores principales: Wiesmann, Veit, Bergler, Matthias, Palmisano, Ralf, Prinzen, Martin, Franz, Daniela, Wittenberg, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357336/
https://www.ncbi.nlm.nih.gov/pubmed/28315633
http://dx.doi.org/10.1186/s12859-017-1591-2
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author Wiesmann, Veit
Bergler, Matthias
Palmisano, Ralf
Prinzen, Martin
Franz, Daniela
Wittenberg, Thomas
author_facet Wiesmann, Veit
Bergler, Matthias
Palmisano, Ralf
Prinzen, Martin
Franz, Daniela
Wittenberg, Thomas
author_sort Wiesmann, Veit
collection PubMed
description BACKGROUND: Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. RESULTS: We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. CONCLUSION: The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1591-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-53573362017-03-22 Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms Wiesmann, Veit Bergler, Matthias Palmisano, Ralf Prinzen, Martin Franz, Daniela Wittenberg, Thomas BMC Bioinformatics Research Article BACKGROUND: Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. RESULTS: We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. CONCLUSION: The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1591-2) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-18 /pmc/articles/PMC5357336/ /pubmed/28315633 http://dx.doi.org/10.1186/s12859-017-1591-2 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wiesmann, Veit
Bergler, Matthias
Palmisano, Ralf
Prinzen, Martin
Franz, Daniela
Wittenberg, Thomas
Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title_full Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title_fullStr Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title_full_unstemmed Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title_short Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
title_sort using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357336/
https://www.ncbi.nlm.nih.gov/pubmed/28315633
http://dx.doi.org/10.1186/s12859-017-1591-2
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