AutoCellSeg

• Verfasst von: Khan, Arif ul Maula [VerfasserIn]
• Verfasst von: Torelli, Angelo [VerfasserIn]
• Verfasst von: Gretz, Norbert [VerfasserIn]
• Titel: AutoCellSeg
• Titelzusatz: robust automatic colony forming unit (CFU)/cell analysis using adaptive image segmentation and easy-to-use post-editing techniques
• Verf.angabe: Arif ul Maula Khan, Angelo Torelli, Ivo Wolf & Norbert Gretz
• E-Jahr: 2018
• Jahr: 08 May 2018
• Umfang: 10 S.
• Fussnoten: Gesehen am 20.07.2018
• Titel Quelle: Enthalten in: Scientific reports
• Ort Quelle: [London] : Springer Nature, 2011
• Jahr Quelle: 2018
• Band/Heft Quelle: 8(2018) Artikel-Nummer 7302, 10 Seiten
• ISSN Quelle: 2045-2322
• DOI: doi:10.1038/s41598-018-24916-9
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1577806646
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

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.