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Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Updated: May 6, 2026

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

DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy.

Cyril Meyer¹, Victor Hanss², Etienne Baudrier³

¹IRIMAS, Université de Haute-Alsace, UR 7499, Mulhouse, France.

Biology of the Cell

|September 1, 2025

Summary

This summary is machine-generated.

DeepSCEM is a new tool for fast and efficient segmentation of cellular electron microscopy images. It uses deep learning to make organelle segmentation easier for researchers.

Keywords:

cellular imaging deep learning electron microscopy organelles segmentation software

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Area of Science:

Cell Biology
Microscopy
Computational Biology

Background:

Deep learning, particularly convolutional neural networks (CNNs), excels at image segmentation.
Automatic segmentation of cellular electron micrographs is crucial for biological research.
Existing tools lack user-friendliness, hindering the adoption of deep learning in electron microscopy.

Purpose of the Study:

To introduce DeepSCEM, a user-friendly tool for segmenting cellular electron microscopy images.
To enable fast and efficient organelle segmentation using deep learning.
To simplify the generation and training of deep learning models for this task.

Main Methods:

Development of DeepSCEM, a straightforward software tool.
Application of deep learning, specifically CNNs, for image segmentation.
Focus on user-friendly model generation and training workflows.

Main Results:

DeepSCEM provides a fast and efficient solution for electron microscopy image segmentation.
The tool facilitates the creation and training of deep learning models for organelle segmentation.
It addresses the need for accessible, specialized software in this field.

Conclusions:

DeepSCEM democratizes the use of deep learning for cellular electron microscopy image analysis.
The tool enhances research efficiency by simplifying complex segmentation tasks.
It promotes wider adoption of advanced computational methods in cell biology.