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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey.

Anusha Aswath¹, Ahmad Alsahaf², Ben N G Giepmans²

¹Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University Groningen, Groningen, The Netherlands; Department of Biomedical Sciences of Cells and Systems, University Groningen, University Medical Center Groningen, Groningen, The Netherlands.

Medical Image Analysis

|August 12, 2023

Summary

Deep learning significantly advances automated segmentation for large-scale electron microscopy (EM) datasets. This review details deep learning methods, datasets, and challenges in segmenting cellular structures from EM images.

Keywords:

Deep learning Electron microscopy Instance Segmentation Self-supervised Semantic Supervised

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Last Updated: Jul 19, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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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:

Microscopy and Imaging Science
Computational Biology
Artificial Intelligence in Biology

Background:

Manual segmentation of large-scale electron microscopy (EM) datasets is labor-intensive and time-consuming.
Automated segmentation methods are essential for efficient analysis of high-resolution cellular and sub-cellular structures.
Deep learning has emerged as a powerful tool for addressing these challenges in recent years.

Purpose of the Study:

To review the progress of deep learning-based segmentation techniques in large-scale cellular EM over the past six years.
To detail key datasets, methodologies (supervised, unsupervised, self-supervised), and challenges in EM image segmentation.
To provide an outlook on future trends, including large-scale models and learning from unlabeled data.

Main Methods:

Comprehensive review of deep learning algorithms applied to 2D and 3D EM image segmentation.
Analysis of supervised, unsupervised, and self-supervised learning approaches.
Examination of network architectures designed to handle EM image complexities like heterogeneity and spatial complexity.

Main Results:

Significant advancements in both semantic and instance segmentation of cellular and sub-cellular structures using deep learning.
Identification of key datasets that have driven progress in EM segmentation.
Description of methods that address challenges such as image heterogeneity and spatial complexity.

Conclusions:

Deep learning has revolutionized automated segmentation in large-scale EM, enabling more efficient and accurate analysis.
Future directions include leveraging large-scale models and unlabeled data for more generalized feature learning across diverse EM datasets.
Continued development in deep learning promises further breakthroughs in understanding cellular and sub-cellular organization.