Visual Analytics for the Exploration of Tumor Tissue Characterization

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dc.contributor.authorRaidou, Renata Georgiaen_US
dc.contributor.authorHeide, Uulke A. van deren_US
dc.contributor.authorDinh, Cuong Vieten_US
dc.contributor.authorGhobadi, Ghazalehen_US
dc.contributor.authorKallehauge, Jesper Follsteden_US
dc.contributor.authorBreeuwer, Marcelen_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorH. Carr, K.-L. Ma, and G. Santuccien_US
dc.date.accessioned2015-05-22T12:50:44Z
dc.date.available2015-05-22T12:50:44Z
dc.date.issued2015en_US
dc.description.abstractTumors are heterogeneous tissues consisting of multiple regions with distinct characteristics. Characterization of these intra-tumor regions can improve patient diagnosis and enable a better targeted treatment. Ideally, tissue characterization could be performed non-invasively, using medical imaging data, to derive per voxel a number of features, indicative of tissue properties. However, the high dimensionality and complexity of this imaging-derived feature space is prohibiting for easy exploration and analysis - especially when clinical researchers require to associate observations from the feature space to other reference data, e.g., features derived from histopathological data. Currently, the exploratory approach used in clinical research consists of juxtaposing these data, visually comparing them and mentally reconstructing their relationships. This is a time consuming and tedious process, from which it is difficult to obtain the required insight. We propose a visual tool for: (1) easy exploration and visual analysis of the feature space of imaging-derived tissue characteristics and (2) knowledge discovery and hypothesis generation and confirmation, with respect to reference data used in clinical research. We employ, as central view, a 2D embedding of the imaging-derived features. Multiple linked interactive views provide functionality for the exploration and analysis of the local structure of the feature space, enabling linking to patient anatomy and clinical reference data. We performed an initial evaluation with ten clinical researchers. All participants agreed that, unlike current practice, the proposed visual tool enables them to identify, explore and analyze heterogeneous intra-tumor regions and particularly, to generate and confirm hypotheses, with respect to clinical reference data.en_US
dc.description.number3en_US
dc.description.sectionheadersBiomedical Visualizationen_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume34en_US
dc.identifier.doi10.1111/cgf.12613en_US
dc.identifier.pages011-020en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12613en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.8 [Computer Graphics]en_US
dc.subjectApplicationsen_US
dc.subjectApplicationsen_US
dc.subjectJ.3 [Computer Applications]en_US
dc.subjectLife and Medical Sciencesen_US
dc.subjectLife and Medical Sciencesen_US
dc.titleVisual Analytics for the Exploration of Tumor Tissue Characterizationen_US
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34-Issue 3
EuroVis15: Eurographics Conference on Visualization