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Updated: Mar 8, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Automatic Segmentation and Quantification of Filamentous Structures in Electron Tomography.

Leandro A Loss¹, George Bebis², Hang Chang¹

¹Life Sciences Division, Lawrence Berkeley Nat Lab.

ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine

|January 17, 2017

Summary

We developed an automated framework for analyzing noisy 3D electron tomography data, enabling precise segmentation and quantification of filamentous structures in biological samples like plant cell walls.

Keywords:

3d segmentation electron tomography plant cell wall tensor voting

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Imaging Replicative Domains in Ultrastructurally Preserved Chromatin by Electron Tomography

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Published on: May 20, 2022

Area of Science:

Microscopy and Imaging Science
Cell Biology
Materials Science

Background:

Electron tomography (ET) offers nanoscale imaging of ultrastructures but faces challenges from noise, staining variability, and beam damage.
Accurate analysis of filamentous structures in 3D ET data is crucial for understanding cellular organization and material properties.

Purpose of the Study:

To develop and validate an automated framework for segmenting and quantifying filamentous objects in 3D electron tomography.
To enable detailed compositional and morphological analysis of complex biological networks.

Main Methods:

A three-step approach involving Hessian filtering for local filament enhancement.
Tensor voting for detection and completion of filamentous structures, including gap filling.
Delineation of filamentous networks for subsequent quantitative analysis.

Main Results:

Successfully validated the framework using synthetic data.
Applied the method to plant cell wall tomograms after various chemical treatments.
Revealed organizational characteristics and polysaccharide-specific effects of different extraction protocols.

Conclusions:

The developed framework provides robust automatic segmentation and quantification of filamentous networks in challenging 3D ET data.
Enables detailed insights into the structural and compositional changes of plant cell walls under different treatments.
Offers a valuable tool for nanoscale analysis in biology and materials science.