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

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High-throughput thickness analysis of 2D materials enabled by intelligent image segmentation.

Jun Chen Ng^1,2, Farina Muhamad², Pauline Shan Qing Yeoh²

¹School of Materials Science and Engineering, Peking University, Beijing 100871, China. khinwee.lai@um.edu.my.

|December 22, 2025

Summary

An AI pipeline estimates 2D material thickness from optical microscopy images, overcoming Atomic Force Microscopy limitations. This AI model offers a faster, non-destructive method for precise thickness measurement.

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Exfoliation and Analysis of Large-area, Air-Sensitive Two-Dimensional Materials

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Published on: January 5, 2019

Area of Science:

Materials Science
Optical Physics
Artificial Intelligence

Background:

Thickness characterization of 2D materials is crucial for understanding their properties.
Existing methods like Atomic Force Microscopy (AFM) have significant limitations including slow speed and artifacts.
A faster, more efficient, and non-destructive method is needed for 2D material thickness measurement.

Purpose of the Study:

To develop an Artificial Intelligence (AI)-based pipeline for estimating 2D material thickness using Optical Microscopy (OM) images.
To compare the performance of different AI models and segmentation techniques for thickness prediction.
To validate the generalizability and identify key features for thickness estimation.

Main Methods:

Utilized optical microscopy images and paired Atomic Force Microscopy (AFM) data for training AI models.
Employed Otsu's thresholding and Cellpose for region of interest (ROI) segmentation.
Trained regression models, including Random Forest Regressor (RFR) and Multi-Layer Perceptron (MLP), on extracted morphological and colour features.
Applied Shapley Additive Explanations (SHAP) for feature importance analysis.

Main Results:

The AI pipeline accurately estimated 2D material thickness from OM images.
The Multi-Layer Perceptron (MLP) model with automated Cellpose segmentation achieved high predictive performance (R² = 0.947).
Statistical analysis confirmed the model's generalizability across different segmentation methods.
SHAP analysis identified red and green light intensities as key predictors, consistent with thin-film interference theory.

Conclusions:

The proposed AI-based pipeline offers a robust, efficient, and non-destructive alternative to AFM for 2D material thickness measurement.
The model demonstrates high accuracy and generalizability, even with small datasets.
This approach enables precise and continuous thickness estimation, advancing 2D material characterization.