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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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Bibliographic Details
Published in: Scientific reports 8(2018) Artikel-Nummer 7302, 10 Seiten
Authors and Corporations: Khan, Arif ul Maula (Author), Torelli, Angelo (Author), Gretz, Norbert (Author)
Other Authors: Torelli, Angelo [Author] • Gretz, Norbert 1954- [Author]
Type of Resource: E-Book Component Part
Language: English
published:
08 May 2018
Series: : Scientific reports, 8(2018) Artikel-Nummer 7302, 10 Seiten
, volume:8
Source: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
ISSN: 2045-2322
Description
Summary: 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.
Item Description: Gesehen am 20.07.2018
Physical Description: 10
ISSN: 2045-2322
DOI: 10.1038/s41598-018-24916-9