Title

CellTrackVis: Analyzing the Performance of Cell Tracking Algorithms

Description

Live-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.,Applications,Weimin Li, Xiang Zhang, Alan Stern, Marc Birtwistle, and Federico Iuricich,

Publication Date

1-1-2022

Publisher

Eurographics

DOI

10.2312/evs.20221103

Document Type

Data Set

Identifier

10.2312/evs.20221103

Embargo Date

1-1-2022

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