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dc.contributor.authorSomarakis, Antoniosen_US
dc.contributor.authorIjsselsteijn, Marieke E.en_US
dc.contributor.authorKenkhuis, Boyden_US
dc.contributor.authorUnen, Vincent vanen_US
dc.contributor.authorLuk, Sietse J.en_US
dc.contributor.authorKoning, Fritsen_US
dc.contributor.authorWeerd, Louise van deren_US
dc.contributor.authorMiranda, Noel F. C. C. deen_US
dc.contributor.authorLelieveldt, Boudewijn P. F.en_US
dc.contributor.authorHöllt, Thomasen_US
dc.contributor.editorOeltze-Jafra, Steffen and Raidou, Renata Georgiaen_US
dc.date.accessioned2021-06-12T11:17:23Z
dc.date.available2021-06-12T11:17:23Z
dc.date.issued2021
dc.identifier.isbn978-3-03868-147-2
dc.identifier.urihttps://doi.org/10.2312/evm.20211074
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evm20211074
dc.description.abstractThe detailed analysis of tissue composition is crucial for the understanding of tissue functionality. For example, the location of immune cells related to a tumour area is highly correlated with the effectiveness of immunotherapy. Therefore, experts are interested in presence of cells with specific characteristics as well as the spatial patterns they form. Recent advances in single-cell imaging modalities, producing high-dimensional, high-resolution images enable the analysis of both of these features. However, extracting useful insight on tissue functionality from these high-dimensional images poses serious and diverse challenges to data analysis. We have developed an interactive, data-driven pipeline covering the main analysis challenges experts face, from the pre-processing of images via the exploration of tissue samples to the comparison of cohorts of samples. All parts of our pipeline have been developed in close collaboration with domain experts and are already a vital part in their daily analysis routine.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman centered computing
dc.subjectVisualization systems and tools
dc.titleVisual Analysis of Tissue Images at Cellular Levelen_US
dc.description.seriesinformationEuroVis 2021 - Dirk Bartz Prize
dc.description.sectionheaders1st Prize
dc.identifier.doi10.2312/evm.20211074
dc.identifier.pages1-5


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