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dc.contributor.authorLi, Weiminen_US
dc.contributor.authorZhang, Xiangen_US
dc.contributor.authorStern, Alanen_US
dc.contributor.authorBirtwistle, Marcen_US
dc.contributor.authorIuricich, Federicoen_US
dc.contributor.editorAgus, Marcoen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorHoellt, Thomasen_US
dc.description.abstractLive-cell imaging is a common data acquisition technique used by biologists to analyze cell behavior. Since manually tracking cells in a video sequence is extremely time-consuming, many automatic algorithms have been developed in the last twenty years to accomplish the task. However, none of these algorithms can yet claim robust tracking performance at the varying of acquisition conditions (e.g., cell type, acquisition device, cell treatments). While many visualization tools exist to help with cell behavior analysis, there are no tools to help with the algorithm's validation. This paper proposes CellTrackVis, a new visualization tool for evaluating cell tracking algorithms. CellTrackVis allows comparing automatically generated cell tracks with ground truth data to help biologists select the best-suited algorithm for their experimental pipeline. Moreover, CellTackVis can be used as a debugging tool while developing a new cell tracking algorithm to investigate where, when, and why each tracking error occurred.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.subjectCCS Concepts: Applied computing --> Bioinformatics; Human-centered computing --> Visualization toolkits
dc.subjectApplied computing
dc.subjectHuman centered computing
dc.subjectVisualization toolkits
dc.titleCellTrackVis: Analyzing the Performance of Cell Tracking Algorithmsen_US
dc.description.seriesinformationEuroVis 2022 - Short Papers
dc.identifier.pages5 pages

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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License