@inproceedings{0c64caa362de433f88650373e0891425,
title = "Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow",
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.",
author = "Veronica Biga and {Alves Coelho}, Olivia and Gokhale, {Paul J.} and James Mason and Eduardo Mendes and Andrews, {Peter W.} and Daniel Coca",
year = "2017",
doi = "10.1007/978-3-319-67834-4_8",
language = "English",
series = "Lecture Notes in Bioinformatics",
publisher = "Springer Nature",
pages = "93--106",
editor = "Andrea Bracciali and Giulio Caravagna and David Gilbert and Roberto Tagliaferri",
booktitle = "Computational intelligence methods for bioinformatics and biostatistics",
address = "United States",
}