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Updated: May 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Automated cell structure extraction for 3D electron microscopy by deep learning.

Jin Kousaka¹, Atsuko H Iwane^2,3,4, Yuichi Togashi^5,6,7

¹Graduate School of Life Sciences, Ritsumeikan University, 1-1-1 Noji-higashi, 525-8577, Kusatsu, Shiga, Japan.

Scientific Reports

|May 20, 2025

Summary

This summary is machine-generated.

This study introduces an automated deep learning system for segmenting and reconstructing 3D cell structures from electron microscopy images. The method significantly reduces manual labor, enabling efficient 3D cell modeling.

Keywords:

Bioimage analysis Cell division FIB-SEM Organelle Segmentation

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Area of Science:

Cell Biology
Biotechnology
Microscopy

Background:

3D cell and tissue modeling is vital in biology.
Manual segmentation of electron microscopy images is time-consuming.
Automated solutions are needed to analyze complex cellular structures.

Purpose of the Study:

To develop a deep learning-based automated system for segmenting biological images.
To enable accurate 3D reconstruction of cells and organelles.
To overcome the bottleneck of manual image analysis.

Main Methods:

Utilized focused ion beam scanning electron microscopy (FIB-SEM) for image acquisition.
Employed a U-Net model for organelle segmentation in single-cell images.
Integrated the Segment Anything Model (SAM) and 3D watershed algorithm for cell extraction and 3D model creation.

Main Results:

Successfully automated the creation of 3D cell models from sequential electron microscopy images.
Demonstrated accurate segmentation of cell organelles using the trained U-Net.
Enabled efficient extraction of individual 3D cell images from large datasets.

Conclusions:

The developed system fully automates 3D cell model creation.
Deep learning and image processing advancements can enhance segmentation accuracy.
This approach accelerates the analysis of cellular structures.