AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques

Abstract In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust im...

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Autores principales:	Arif ul Maula Khan, Angelo Torelli, Ivo Wolf, Norbert Gretz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b583ddcad5d04c3c87d4e853eb3dcedb
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spelling	oai:doaj.org-article:b583ddcad5d04c3c87d4e853eb3dcedb2021-12-02T12:32:10ZAutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques10.1038/s41598-018-24916-92045-2322https://doaj.org/article/b583ddcad5d04c3c87d4e853eb3dcedb2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24916-9https://doaj.org/toc/2045-2322Abstract In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.Arif ul Maula KhanAngelo TorelliIvo WolfNorbert GretzNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arif ul Maula Khan Angelo Torelli Ivo Wolf Norbert Gretz AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
description	Abstract In biological assays, automated cell/colony segmentation and counting is imperative owing to huge image sets. Problems occurring due to drifting image acquisition conditions, background noise and high variation in colony features in experiments demand a user-friendly, adaptive and robust image processing/analysis method. We present AutoCellSeg (based on MATLAB) that implements a supervised automatic and robust image segmentation method. AutoCellSeg utilizes multi-thresholding aided by a feedback-based watershed algorithm taking segmentation plausibility criteria into account. It is usable in different operation modes and intuitively enables the user to select object features interactively for supervised image segmentation method. It allows the user to correct results with a graphical interface. This publicly available tool outperforms tools like OpenCFU and CellProfiler in terms of accuracy and provides many additional useful features for end-users.
format	article
author	Arif ul Maula Khan Angelo Torelli Ivo Wolf Norbert Gretz
author_facet	Arif ul Maula Khan Angelo Torelli Ivo Wolf Norbert Gretz
author_sort	Arif ul Maula Khan
title	AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
title_short	AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
title_full	AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
title_fullStr	AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
title_full_unstemmed	AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
title_sort	autocellseg: robust automatic colony forming unit (cfu)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/b583ddcad5d04c3c87d4e853eb3dcedb
work_keys_str_mv	AT arifulmaulakhan autocellsegrobustautomaticcolonyformingunitcfucellanalysisusingadaptiveimagesegmentationandeasytouseposteditingtechniques AT angelotorelli autocellsegrobustautomaticcolonyformingunitcfucellanalysisusingadaptiveimagesegmentationandeasytouseposteditingtechniques AT ivowolf autocellsegrobustautomaticcolonyformingunitcfucellanalysisusingadaptiveimagesegmentationandeasytouseposteditingtechniques AT norbertgretz autocellsegrobustautomaticcolonyformingunitcfucellanalysisusingadaptiveimagesegmentationandeasytouseposteditingtechniques
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AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques

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