Bio-EdIP: An automatic approach for in vitro cell confluence images quantification. (July 2017)

• Record Type: Journal Article
• Authors: Cardona, Andrés; Ariza-Jiménez, Leandro; Uribe, Diego; Arroyave, Johanna C.; Galeano, July; Cortés-Mancera, Fabian M.
• Is Part Of: Computer methods and programs in biomedicine. Volume 145(2017)
• Page Start: 23
• Page End: 33
• Publication Date: 2017-07
• Subjects: Cell culture -- Image processing -- Morphological reconstruction -- Segmentation -- User-friendly interface; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.026
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Bio-EdIP exhibits a better performance than TScratch in different in vitro cell culture images. Bio-EdIP improves selection of starting pixels and processing time. Bio-EdIP is an objective tool to quantify confluence levels and processes of cell growth. This software tool is freely available with a friendly-user interface (http://be.itm.edu.co/). New manually annotated images have been generated for algorithms evaluation. Abstract: Background and objectives: Cell imaging is a widely-employed technique to analyze multiple biological processes. Therefore, simple, accurate and quantitative tools are needed to understand cellular events. For this purpose, Bio-EdIP was developed as a user-friendly tool to quantify confluence levels using cell culture images. Methods: The proposed algorithm combines a pre-processing step with subsequent stages that involve local processing techniques and a morphological reconstruction-based segmentation algorithm. Segmentation performance was assessed in three constructed image sets, comparing F-measure scores and AUC values (ROC analysis) forBio-EdIP, its previous version and TScratch. Furthermore, segmentation results were compared with published algorithms using eight public benchmarks. Results: Bio-EdIP automatically segmented cell-free regions from images of in vitro cell culture. Based on mean F-measure scores and ROC analysis, Bio-EdIP conserved a high performance regardless of image characteristics of the constructed dataset, when compared with its previous version and TScratch. Although acquisition quality of the public dataset affected Bio-EdIP segmentation, performance was better in two out of eight public sets. Conclusions: Bio-EdIP is a user-friendly interface, which is useful for the automatic analysis of confluence levels and cell growth processes using in vitro cell culture images. Here, we also presented new manually annotated data for algorithms evaluation.