Biga, V., Alves Coelho, O.M., Gokhale, P.J. et al. (4 more authors) (2017) Statistical texture-based mapping of cell differentiation under microfluidic flow. In: Bracciali , A., Caravagna, G., Gilbert, D. and Tagliaferri, R., (eds.) CIBB 2016: Computational Intelligence Methods for Bioinformatics and Biostatistics. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 01-03 Sep 2016, Stirling, United Kingdom. Lecture Notes in Computer Science (vol 10477). Springer International Publishing , pp. 93-106. ISBN 9783319678337
Abstract
Timelapse microscopy enables long term monitoring of biological processes, however a major bottleneck in assesing experimental outcome is the need for an automated analysis framework to extract statistics and evaluate results. In this study, we use Gabor energy texture descriptors to generate a high dimensional feature space which is analysed with principal component analysis to provide unsupervised characterisation of texture differences between pairs of images. We apply this technique to differentiation of human embryonic carcinoma cells in the presence of all-trans retinoic acid (RA) and show that differentiation outcome can be predicted directly from texture information. A microfluidic environment is used to deliver pulses of RA stimulation over five days in culture. Results provide insight into the dynamics of cell response to differentiation signals over time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author-produced version of a paper accepted for publication in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Principal component analysis; Texture features; Gabor energy; Fate mapping; Cell differentiation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Biomedical Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Nov 2017 10:22 |
Last Modified: | 19 Dec 2022 13:48 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-67834-4_8 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123944 |