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A growing number of screening applications require the automated monitoring of cell populations in a high-throughput, high-content environment. These applications depend on accurate cell tracking of individual cells that display various behaviors including mitosis, occlusion, rapid movement, and entering and leaving the field of view. We present a tracking approach that explicitly models each of these behaviors and represents the association costs in a graph-theoretic minimum-cost flow framework. We show how to extend the minimum-cost flow algorithm to account for mitosis and merging events by coupling particular edges. We applied the algorithm to nearly 6,000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells. Our algorithm is able to track cells and detect different cell behaviors with an accuracy of over 99%.

Type

Journal article

Journal

Inf Process Med Imaging

Publication Date

2009

Volume

21

Pages

374 - 385

Keywords

Algorithms, Artificial Intelligence, Cells, Cultured, Flow Cytometry, Image Enhancement, Image Interpretation, Computer-Assisted, Microscopy, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity