A Survey of Visualization for Live Cell Imaging

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dc.contributor.authorPretorius, A. J.en_US
dc.contributor.authorKhan, I. A.en_US
dc.contributor.authorErrington, R. J.en_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2017-03-13T18:13:00Z
dc.date.available2017-03-13T18:13:00Z
dc.date.issued2017
dc.description.abstractLive 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 visualization. Nonetheless, visualization 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 visualization developers to evaluate the advantages and disadvantages of different visualization methods, and for visualization researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualization methods for live cell imaging from a visualization research perspective for the first time. Based on recent visualization theory, we perform a structured qualitative analysis of visualization methods that includes characterizing 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 visualization; 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.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 visualization. Nonetheless, visualization 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 visualization developers to evaluate the advantages and disadvantages of different visualization methods, and for visualization researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualization methods for live cell imaging from a visualization research perspective for the first time.en_US
dc.description.documenttypestar
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.12784
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12784
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12784
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectvisualization
dc.subjectinformation visualization
dc.subjectmedical imaging
dc.subjectI.3.8 [Computer Graphics]: Applications‐Visualization of phenotypic data derived from live cell imaging
dc.subjectI.3.8 [Computer Graphics]: Applications‐Dynamic data visualization
dc.titleA Survey of Visualization for Live Cell Imagingen_US
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