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Trajectory Energy Minimisation for Cell Growth Tracking and Genealogy Analysis

Hu, Yin, Wang, Su, Ma, N, Hingley-Wilson, Suzie, Rocco, Andrea, McFadden, Johnjoe and Tang, Hongying (2017) Trajectory Energy Minimisation for Cell Growth Tracking and Genealogy Analysis Royal Society Open Science, 4, 170207.

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Abstract

Cell growth experiments with a microfluidic device produce large scale time-lapse image data, which contain important information on cell growth and patterns in their genealogy. To extract such information, we propose a scheme to segment and track bacterial cells automatically. In contrast to most published approaches, which often split segmentation and tracking into two independent procedures, we focus on designing an algorithm that describes cell properties evolving between consecutive frames by feeding segmentation and tracking results from one frame to the next one. The cell boundaries are extracted by minimising the Distance Regularised Level Set Evolution model. Each individual cell was identified and tracked by identifying cell septum and membrane as well as developing a trajectory energy minimisation function along time-lapse series. Experiments show that by applying this scheme, cell growth and division can be measured automatically. The results show the efficiency of the approach when testing on different datasets while comparing with other existing algorithms. The proposed approach demonstrates great potential for large scale bacterial cell growth analysis.

Item Type: Article
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Hu, Yinyin.hu@surrey.ac.ukUNSPECIFIED
Wang, Sus.u.wang@surrey.ac.ukUNSPECIFIED
Ma, NUNSPECIFIEDUNSPECIFIED
Hingley-Wilson, Suzies.hingley-wilson@surrey.ac.ukUNSPECIFIED
Rocco, AndreaA.Rocco@surrey.ac.ukUNSPECIFIED
McFadden, JohnjoeJ.Mcfadden@surrey.ac.ukUNSPECIFIED
Tang, HongyingH.Tang@surrey.ac.ukUNSPECIFIED
Date : 24 May 2017
Funders : Biotechnology and Biological Sciences Research Council (BBSRC)
Identification Number : 10.1098/rsos.170207
Copyright Disclaimer : © 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/ by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Uncontrolled Keywords : Image processing, Time-lapse image analysis, Cell segmentation and tracking, E. coli, Level set framework, Trajectory energy minimisation
Depositing User : Symplectic Elements
Date Deposited : 12 May 2017 09:50
Last Modified : 18 Jul 2017 08:17
URI: http://epubs.surrey.ac.uk/id/eprint/814159

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