Automated Cell Segmentation of Fission Yeast Phase Images

Jennifer O'Brien, Sanaul Hoque, Daniel Mulvihill, Konstantinos Sirlantzis

2017

Abstract

Robust image analysis is an important aspect of all cell biology studies. The geometrics of cells are critical for developing an understanding of biological processes. Time constraints placed on researchers lead to a narrower focus on what data are collected and recorded from an experiment, resulting in a loss of data. Currently, preprocessing of microscope images is followed by the utilisation and parameterisation of inbuilt functions of various softwares to obtain information. Using the fission yeast, Schizosaccharomyes pombe, we propose a novel, fully automated, segmentation software for cells with a significantly lower rate of segmentation errors than PombeX with the same dataset.

Paper Citation

in Harvard Style

O'Brien J., Hoque S., Mulvihill D. and Sirlantzis K. (2017). Automated Cell Segmentation of Fission Yeast Phase Images. In - BIOIMAGING, (BIOSTEC 2017) ISBN , pages 0-0. DOI: 10.5220/0006149100001488

in Bibtex Style

@conference{bioimaging17,
author={Jennifer O'Brien and Sanaul Hoque and Daniel Mulvihill and Konstantinos Sirlantzis},
title={Automated Cell Segmentation of Fission Yeast Phase Images},
booktitle={ - BIOIMAGING, (BIOSTEC 2017)},
year={2017},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006149100001488},
isbn={},
}

in EndNote Style

TY - CONF

JO - - BIOIMAGING, (BIOSTEC 2017)
TI - Automated Cell Segmentation of Fission Yeast Phase Images
SN -
AU - O'Brien J.
AU - Hoque S.
AU - Mulvihill D.
AU - Sirlantzis K.
PY - 2017
SP - 0
EP - 0
DO - 10.5220/0006149100001488