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Gastrulation

Gastrulation

Gastrulation establishes the three primary tissues of an embryo: the ectoderm, mesoderm, and endoderm. This developmental process relies on a series of intricate cellular movements, which in humans transforms a flat, “bilaminar disc” composed of two cell sheets into a three-tiered structure. In the resulting embryo, the endoderm serves as the bottom layer, and stacked directly above it is the intermediate mesoderm, and then the uppermost ectoderm. Respectively, these tissue strata...

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Related Experiment Video

Updated: Jun 27, 2025

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Published on: October 17, 2011

Deep Learning-Based Cell Tracking in Deforming Organs and Moving Animals.

¹RIKEN Center for Biodynamic Research, Kobe, Japan. chentao.wen@riken.jp.

Methods in Molecular Biology (Clifton, N.J.)

|May 6, 2024

Summary

This summary is machine-generated.

Accurate cell tracking in moving organisms is crucial for biological research. Deep learning methods, like 3DeeCellTracker, now enable precise cell tracking even with complex movements and low-quality images.

Keywords:

3D time-lapse images Cell tracking Deep learning Deforming organs Moving animals

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Related Experiment Videos

Last Updated: Jun 27, 2025

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Tracking Morphogenetic Tissue Deformations in the Early Chick Embryo

Published on: October 17, 2011

Tracking Mouse Bone Marrow Monocytes In Vivo

Tracking Mouse Bone Marrow Monocytes In Vivo

Published on: February 27, 2015

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

Area of Science:

* Biological imaging and analysis
* Computational biology
* Deep learning applications in life sciences

Background:

* Cell tracking is vital for understanding biological functions in dynamic organs like the brain and heart.
* Significant challenges exist in tracking cells due to organ deformation and whole-body motion, impacting image quality.
* Deep learning offers advanced solutions for cell detection and position estimation in challenging imaging scenarios.

Purpose of the Study:

* To address the challenges of cell tracking in deforming organs and moving animals.
* To present a detailed protocol for cell segmentation and tracking using 3DeeCellTracker.
* To facilitate deeper insights into organ dynamics and biological processes through improved cell tracking.

Main Methods:

* Utilizing deep learning techniques for cell detection in low-quality images.
* Employing advanced algorithms for estimating cell positions during large nonrigid movements.
* Implementing a detailed protocol for data preparation, cell segmentation, and tracking with 3DeeCellTracker.

Main Results:

* Demonstrated the capability of deep learning to overcome challenges in cell tracking within dynamic biological systems.
* Provided a robust protocol for accurate cell segmentation and tracking in complex environments.
* Enabled researchers to obtain high-quality data from challenging time-lapse cell imaging.

Conclusions:

* Deep learning-based cell tracking significantly enhances the ability to study cellular dynamics in vivo.
* The 3DeeCellTracker protocol offers a powerful tool for researchers investigating organ dynamics.
* This approach is expected to advance our understanding of fundamental biological processes in moving organisms.