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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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A Lightweight Data-Augmented Deep Learning Framework for Real-Time Instance Segmentation in Liquid-Phase In Situ

Ming-Hao Shen¹, Wei-Che Chang¹, Wen-Huei Chu²

¹Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 70101, Taiwan.

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Summary

This summary is machine-generated.

This study introduces a deep learning framework for liquid-phase transmission electron microscopy (LPTEM) to overcome data limitations. It enables efficient, real-time nanoparticle analysis for accelerated materials discovery.

Keywords:

data augmentation deep learning image segmentation nanoparticle object detection transmission electron microscopy

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Area of Science:

Materials Science
Electron Microscopy
Artificial Intelligence

Background:

Liquid-phase transmission electron microscopy (LPTEM) provides crucial data on material dynamics.
Automated LPTEM analysis is limited by scarce annotated data and high image acquisition costs.

Purpose of the Study:

To develop an annotation-efficient deep learning framework for LPTEM.
To enable real-time detection and segmentation of nanoparticles in LPTEM.
To accelerate materials discovery through enhanced in situ analysis.

Main Methods:

Utilized CycleGAN for synthetic LPTEM image generation to expand datasets.
Employed a fine-tuned YOLOv11n model for nanoparticle detection.
Implemented Mobile-UNet variants for high-resolution instance segmentation.

Main Results:

CycleGAN generated style-consistent synthetic LPTEM images, effectively augmenting the dataset.
YOLOv11n achieved 97.66% precision and 99.05% mAP50 for nanoparticle detection.
Mobile-UNet variants demonstrated high instance segmentation accuracy with IoU scores up to 0.9207.
The integrated YOLO + Mobile-UNet Slim achieved real-time processing at 102.34 FPS.

Conclusions:

The proposed deep learning framework significantly improves real-time nanoparticle detection and segmentation in LPTEM.
This approach offers a scalable and annotation-efficient solution for high-precision LPTEM analysis.
The framework facilitates autonomous in situ experiments and accelerates materials discovery.