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

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Semiautomatic contour tracking method for biological object segmentation in thin-section electron microscope images

Gen Maeda¹, Misuzu Baba², Norio Baba^1,2

¹Major of Informatics, Graduate School, Kogakuin University, 2665-1 Nakano, Hachioji, Tokyo 192-0015, Japan.

Microscopy (Oxford, England)

|February 28, 2023

Summary

A new Gabor wavelet method accurately segments electron microscopic images without manual drawing. This artificial intelligence (AI) tool aids in creating training data and analyzing images more efficiently.

Keywords:

Gabor wavelets biological object segmentation contour tracking image analysis

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Area of Science:

Electron microscopic image processing
Artificial intelligence (AI) in image segmentation
Computational imaging

Background:

Artificial intelligence (AI) is crucial for electron microscopic image segmentation.
Manual creation of training data for AI is time-consuming and laborious.
Existing Gabor filter methods have limitations in detecting specific contours like ridges and step edges.

Purpose of the Study:

To develop a simple, accurate, and automated segmentation tool for electron microscopic images.
To create training data for AI without manual intervention.
To support immediate image analysis in electron microscopy.

Main Methods:

Devised a Gabor wavelet-based contour tracking method.
Developed modified zero DC component-type Gabor wavelets, overcoming limitations of previous nonzero DC component wavelets.
Applied the method to segment contours in thin-section transmission electron microscope images.

Main Results:

The proposed method accurately tracks membrane-like ridges, step edges, and pattern boundary contours.
The modified Gabor wavelets effectively confine wavelet analysis to small image areas.
Demonstrated practical applicability for segmenting organelle contours in freeze-substituted electron microscopy images.

Conclusions:

The developed Gabor wavelet-based method offers an effective and automated solution for electron microscopic image segmentation.
This tool significantly reduces the burden of manual training data creation for AI.
The method enhances the efficiency and accuracy of image analysis in electron microscopy.