Coupled Minimum-Cost Flow Cell Tracking

Authors:
Dirk Padfield;Jens Rittscher;Badrinath Roysam
Affiliations:
GE Global Research, One Research Circle, Niskayuna, NY 12309 and Rensselaer Polytechnic Institute, Troy, NY 12180;GE Global Research, One Research Circle, Niskayuna, NY 12309;Rensselaer Polytechnic Institute, Troy, NY 12180
Venue:
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Year:
2009

Citing 5
Cited 2

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Introduction to Algorithms

Introduction to Algorithms
Cell segmentation, tracking, and mitosis detection using temporal context

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces

IEEE Transactions on Image Processing
Multiple particle tracking in 3-D+t microscopy: method and application to the tracking of endocytosed quantum dots

IEEE Transactions on Image Processing

A multiple object geometric deformable model for image segmentation

Computer Vision and Image Understanding
Cell tracking in microscopic video using matching and linking of bipartite graphs

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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%.