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Related Experiment Video

Updated: Oct 18, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Automatic segmentation tool for 3D digital rocks by deep learning.

Johan Phan^1,2, Leonardo C Ruspini^3,4, Frank Lindseth⁵

¹Department of Computer Science, NTNU, Trondheim, Norway. johan.phan@ntnu.no.

Scientific Reports

|September 28, 2021

Summary

This summary is machine-generated.

A new deep learning tool automates micro-CT image segmentation, overcoming noise and artifacts common in geological imaging. This method provides robust, consistent results, outperforming human experts and validating experimental porosity measurements.

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Last Updated: Oct 18, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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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:

Geoscience
Image Analysis
Artificial Intelligence

Background:

Accurate segmentation of micro-CT images is challenging due to noise and artifacts.
Existing methods are time-consuming, require expertise, and are sensitive to image imperfections.

Purpose of the Study:

To develop a fully automated, one-step segmentation tool for micro-CT images.
To improve robustness against various noise types and artifacts.
To provide consistent and accurate segmentation for geological applications.

Main Methods:

Developed a deep learning model trained on diverse 3D micro-CT datasets.
Employed a domain-specific augmented training pipeline with synthetic noise and artifacts.
Validated performance using synthetic datasets and compared with expert segmentations on real rock images.

Main Results:

The tool achieves robust and accurate segmentation of micro-CT images, handling common noise and artifacts effectively.
Outperforms current software and human experts in consistency and robustness against noise.
Model-derived porosity measurements show strong agreement with experimental laboratory data.

Conclusions:

The developed deep learning tool offers a significant advancement in automated micro-CT image segmentation.
It provides a reliable and efficient solution for analyzing complex geological samples.
The method demonstrates potential for accurate quantification of physical properties like porosity.