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Related Experiment Video

Updated: Sep 28, 2025

Concentric Gel System to Study the Biophysical Role of Matrix Microenvironment on 3D Cell Migration

Concentric Gel System to Study the Biophysical Role of Matrix Microenvironment on 3D Cell Migration

Published on: April 3, 2015

Tracking Moving Cells in 3D Time Lapse Images Using 3DeeCellTracker.

Chentao Wen¹, Koutarou D Kimura^1,2,3

¹Graduate School of Science, Nagoya City University, Nagoya, Japan.

|March 28, 2022

Summary

This summary is machine-generated.

We developed 3DeeCellTracker, a deep learning tool for precise cell tracking in 3D time-lapse microscopy images. This software overcomes bottlenecks in analyzing complex cell movements across different biological samples.

Keywords:

3D microscopy Cell segmentation Cell tracking Deep learning Time lapse images

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Last Updated: Sep 28, 2025

Concentric Gel System to Study the Biophysical Role of Matrix Microenvironment on 3D Cell Migration

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Published on: April 3, 2015

Analysis of Cell Migration within a Three-dimensional Collagen Matrix

Analysis of Cell Migration within a Three-dimensional Collagen Matrix

Published on: October 5, 2014

Live Imaging of Primary Cerebral Cortex Cells Using a 2D Culture System

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Published on: August 9, 2017

Area of Science:

Bioimaging and Cell Biology
Computational Biology and Bioinformatics

Background:

Advanced 3D microscopy allows monitoring of multiple cells in diverse biological samples.
Segmenting and tracking dynamic cell movements in 3D time-lapse (3D + T) images is a significant challenge.

Purpose of the Study:

To introduce 3DeeCellTracker, a novel deep learning-based software pipeline.
To provide a protocol for precise cell segmentation and tracking in 3D + T images, even with large cell movements and varied optical systems.

Main Methods:

Development of a deep learning-based software pipeline, 3DeeCellTracker.
Detailed protocol for computational environment setup, data requirements, and cell tracking procedures.

Main Results:

3DeeCellTracker enables precise tracking of cells exhibiting large movements in 3D + T images.
The software is effective across different animal/organ samples and diverse optical imaging systems.

Conclusions:

3DeeCellTracker significantly enhances the analysis of cell dynamics in challenging 3D + T image datasets.
This protocol facilitates the study of cell activities and movements previously difficult to quantify.