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Fast processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre. (Cancer Pharmacology and Computational Medicine, Department of Medical Sciences)
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A fast computational framework for large-scale parallel processing of label-free video microscopy movies of human and bacterial cell populations growing in vitro during chemical exposure was developed in MATLAB®. The overarching aim was to quantify and study time evolving morphological effects due to chemical perturbations caused by single drugs and combinations. Using this framework, a previously reported method for characterization of differences in time evolving morphologies of human cell populations, based on pixel histogram hierarchies of phase-contrast microscopy images, was re-implemented, refined and subsequently optimized with respect to method-specific tuning parameters. This implementation  was also generalized for time-lapse microscopy movies of bacterial cell cultures, generated by the oCelloScope™ system, which acquires multiple series of images of non-adherent cell populations in the cell culture medium. In addition, a separate computational framework for large-scale parallel quantification of the bacterial growth was deployed as an alternative to the growth kinetics analysis provided by the integrated commercial software of the oCelloScope™ system. The potential of the implemented frameworks was demonstrated on experimental data by processing time-lapse movies from different human and bacterial cell populations, while being exposed to different single chemical compounds and combinations. These novel computational tools are compatible with either single high-end multi-core computers or cloud-based distributed computing infrastructures offered via MapReduce, and Hadoop® MapReduce, respectively. This enables fast and fault-tolerant processing of huge video microscopy datasets and opens for optimization of user-defined tuning parameters.

Place, publisher, year, edition, pages
2016. , 118 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-303946OAI: oai:DiVA.org:uu-303946DiVA: diva2:974708
Educational program
Master Programme in Bioinformatics
Presentation
2016-09-20, C4:301, BMC, 13:00 (English)
Supervisors
Available from: 2016-09-27 Created: 2016-09-27 Last updated: 2016-09-27Bibliographically approved

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