UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Development of an image processing method for automated, non-invasive and scale-independent monitoring of adherent cell cultures

Jaccard, N; (2015) Development of an image processing method for automated, non-invasive and scale-independent monitoring of adherent cell cultures. Doctoral thesis , UCL (University College London). Green open access

[thumbnail of Thesis_NJ_Final_New.pdf] PDF
Thesis_NJ_Final_New.pdf

Download (55MB)

Abstract

Adherent cell culture is a key experimental method for biological investigations in diverse areas such as developmental biology, drug discovery and biotechnology. Light microscopy-based methods, for example phase contrast microscopy (PCM), are routinely used for visual inspection of adherent cells cultured in transparent polymeric vessels. However, the outcome of such inspections is qualitative and highly subjective. Analytical methods that produce quantitative results can be used but often at the expense of culture integrity or viability. In this work, an imaging-based strategy to adherent cell cultures monitoring was investigated. Automated image processing and analysis of PCM images enabled quantitative measurements of key cell culture characteristics. Two types of segmentation algorithms for the detection of cellular objects on PCM images were evaluated. The first one, based on contrast filters and dynamic programming was quick (<1s per 1280×960 image) and performed well for different cell lines, over a wide range of imaging conditions. The second approach, termed ‘trainable segmentation’, was based on machine learning using a variety of image features such as local structures and symmetries. It accommodated complex segmentation tasks while maintaining low processing times (<5s per 1280×960 image). Based on the output from these segmentation algorithms, imaging-based monitoring of a large palette of cell responses was demonstrated, including proliferation, growth arrest, differentiation, and cell death. This approach is non-invasive and applicable to any transparent culture vessel, including microfabricated culture devices where a lack of suitable analytical methods often limits their applicability. This work was a significant contribution towards the establishment of robust, standardised, and affordable monitoring methods for adherent cell cultures. Finally, automated image processing was combined with computer-controlled cultures in small-scale devices. This provided a first demonstration of how adaptive culture protocols could be established; i.e. culture protocols which are based on cellular response instead of arbitrary time points.

Type: Thesis (Doctoral)
Title: Development of an image processing method for automated, non-invasive and scale-independent monitoring of adherent cell cultures
Open access status: An open access version is available from UCL Discovery
Language: English
Keywords: Stem cells, image processing, machine vision, machine learning, segmentation, microscopy, phase contrast microscopy, cell culture, monitoring
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/1461036
Downloads since deposit
899Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item