Pretorius, AJ, Khan, IA and Errington, RJ (2017) A survey of visualisation for live cell imaging. Computer Graphics Forum, 36 (1). pp. 46-63. ISSN 0167-7055
Abstract
Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualisation. Nonetheless, visualisation methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualisation developers to evaluate the advantages and disadvantages of different visualisation methods, and for visualisation researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualisation methods for live cell imaging from a visualisation research perspective for the first time. Based on recent visualisation theory, we perform a structured qualitative analysis of visualisation methods that includes characterising the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualisation; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Authors, Computer Graphics Forum. © 2016 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Pretorius, A. J., Khan, I. A. and Errington, R. J. (2016), A Survey of Visualization for Live Cell Imaging. Computer Graphics Forum.; which has been published in final form at https://dx.doi.org/10.1111/cgf.12784. This article may be used for non-commercial purposes in accordance with the Wiley Terms and Conditions for Self-Archiving. |
Keywords: | visualization; information visualization; medical imaging |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) |
Funding Information: | Funder Grant number Leverhulme Trust ECF-2012-071 |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Nov 2015 12:56 |
Last Modified: | 30 Jun 2020 14:48 |
Published Version: | https://dx.doi.org/10.1111/cgf.12784 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/cgf.12784 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:91447 |