Lineage mapper: A versatile cell and particle tracker.

Joe Chalfoun,Mary Brady,Michael Majurski,Michael Halter,Kiran Bhadriraju,Alden Dima

doi:10.1038/srep36984

Abstract

The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.

Highlights

The ability to accurately track cells and particles from images is critical to many biomedical problems
Automated microscopy has facilitated the large scale acquisition of live cell image data[1] to monitor migration, morphology, and lineage tracing of large numbers of single cells or colonies in culture
A typical workflow to quantify single cell dynamics begins with segmentation, followed by tracking the segmented masks, and extracting dynamic tracking outputs, from which all post-processing analysis can be derived

Summary

Introduction

The ability to accurately track cells and particles from images is critical to many biomedical problems. LM computes a cost function between cells from consecutive frames, detects cell collisions and separates cells by modifying input images, performs mitosis event detection, assigns tracks between cells, and creates the tracking outputs.

Results

Conclusion