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Related Experiment Video

Updated: Mar 12, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

Lineage mapper: A versatile cell and particle tracker.

Joe Chalfoun¹, Michael Majurski¹, Alden Dima¹

¹Information Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg MD, 20899, USA.

Scientific Reports

|November 18, 2016

Summary

This summary is machine-generated.

Lineage Mapper is a new open-source tool that accurately tracks cells and particles in images. It handles complex scenarios like cell division and clumping, offering scalable solutions for large datasets.

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Area of Science:

Biomedical imaging
Cell biology
Bioinformatics

Background:

Accurate cell and particle tracking from images is essential for numerous biomedical research areas.
Existing tracking methods may face challenges with complex biological samples, such as confluent cell populations or segmented clumps.
Scalability for large image datasets is a significant consideration in biological image analysis.

Purpose of the Study:

To develop and introduce Lineage Mapper, an open-source software for robust cell and particle tracking in time-lapse microscopy.
To provide a versatile tracking solution that is independent of the image segmentation method used.
To address limitations in current tracking software, including handling of mitotic events in confluence and separation of incorrectly segmented cell clusters.

Main Methods:

Lineage Mapper was developed as an open-source tracker compatible with ImageJ and MATLAB.
The software is designed to track objects irrespective of the segmentation algorithm applied.
It incorporates algorithms to detect mitosis, resolve cell clumps, and reconstruct cell division and/or fusion lineages.

Main Results:

Lineage Mapper demonstrates accurate and scalable tracking performance, even on terabyte-sized datasets.
The tool successfully handles challenging scenarios, including confluent cells and segmented cell clumps.
Validated on diverse biological and simulated datasets, confirming its reliability and broad applicability.

Conclusions:

Lineage Mapper provides a powerful, open-source solution for accurate cell and particle tracking in biological imaging.
Its ability to handle complex segmentation issues and large datasets makes it a valuable tool for researchers.
The software facilitates the reconstruction of detailed cell division and fusion histories, advancing lineage analysis.