Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis

By Dirk Ryan Padfield, Jens Rittscher, B. Roysam. 2011

162 citations recorded by the source corpus.

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, merging, rapid movement, and entering and leaving the field of view. Many approaches to cell tracking have been developed in the past, but most are quite complex, require extensive post-processing, and are parameter intensive. To overcome such issues, we present a general, consistent, and extensible tracking approach that explicitly models cell behaviors in a graph-theoretic framework. We introduce a way of extending the standard minimum-cost flow algorithm to account for mitosis and merging events through a coupling operation on particular edges. We then show how the resulting graph can be efficiently solved using algorithms such as linear programming to choose the edges of the graph that observe the constraints while leading to the lowest overall cost. This tracking algorithm relies on accurate denoising and segmentation steps for which we use a wavelet-based approach that is able to accurately segment cells even in images with very low contrast-to-noise. In addition, the framework is able to measure and correct for microscope defocusing and stage shift. We applied the algorithms on nearly 6000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells. Our algorithm was able to segment and track cells and detect different cell behaviors with an accuracy of over 99%. This overall framework enables accurate quantitative analysis of cell events and provides a valuable tool for high-throughput biological studies.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

A benchmark for comparison of cell tracking algorithms (2014, background): “Received on August 29, 2013; revised on January 15, 2014; accepted on January 31, 2014”
Automated and semi‐automated cell tracking: addressing portability challenges (2011, methodology): “Moreover, note that in the videos, the number of cell moves tends to be much larger (often by several orders of magnitude) than the number of divisions (Li et al. , 2008; Hawkins et al. , 2009; Padfield et al. , 2010).”
Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model (2013, background): “Nevertheless, with increasing cell density, the temporal association step often requires sophisticated strategies to deal with one-to-many and many-to-one matching problems effectively [12], [13], [22], [23].”
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[184] 5 time-lapse image datasets wavelet-based watershed precision: 99.”