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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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| Veröffentlicht in: | Scientific reports 8(2018) Artikel-Nummer 7302, 10 Seiten |
|---|---|
| Personen und Körperschaften: | , , |
| Titel: | AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques/ Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
08 May 2018
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| Gesamtaufnahme: |
: Scientific reports, 8(2018) Artikel-Nummer 7302, 10 Seiten
, volume:8 |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
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| 520 | |a 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. | ||
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| contents | 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. |
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| spelling | Khan, Arif ul Maula 1983- VerfasserIn (DE-588)113663424X (DE-627)893342785 (DE-576)490724361 aut, AutoCellSeg robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz, 08 May 2018, 10, Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, Gesehen am 20.07.2018, 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., Torelli, Angelo VerfasserIn (DE-588)1163066958 (DE-627)1027302734 (DE-576)507777689 aut, Gretz, Norbert 1954- VerfasserIn (DE-588)1020104589 (DE-627)691105154 (DE-576)359544843 aut, Enthalten in Scientific reports [London] : Macmillan Publishers Limited, part of Springer Nature, 2011 8(2018) Artikel-Nummer 7302, 10 Seiten Online-Ressource (DE-627)663366712 (DE-600)2615211-3 (DE-576)346641179 2045-2322 nnns, volume:8 year:2018 extent:10, http://dx.doi.org/10.1038/s41598-018-24916-9 Verlag Resolving-System kostenfrei Volltext, https://www.nature.com/articles/s41598-018-24916-9 Verlag kostenfrei Volltext, http://dx.doi.org/10.1038/s41598-018-24916-9 LFER, LFER 2018-08-13T00:00:00Z |
| spellingShingle | Khan, Arif ul Maula, Torelli, Angelo, Gretz, Norbert, AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques, 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. |
| swb_id_str | 507806646 |
| title | AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques |
| title_auth | 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 Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz |
| title_fullStr | AutoCellSeg robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz |
| title_full_unstemmed | AutoCellSeg robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz |
| title_in_hierarchy | AutoCellSeg: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques / Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz, |
| title_short | AutoCellSeg |
| title_sort | autocellseg robust automatic colony forming unit cfu cell analysis using adaptive image segmentation and easy to use post editing techniques |
| title_sub | robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques |
| url | http://dx.doi.org/10.1038/s41598-018-24916-9, https://www.nature.com/articles/s41598-018-24916-9 |