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Related Experiment Video

Updated: Nov 22, 2025

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Sequential semi-supervised segmentation for serial electron microscopy image with small number of labels.

Eichi Takaya1, Yusuke Takeichi2, Mamiko Ozaki3

  • 1School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, Kanagawa, Japan.

Journal of Neuroscience Methods
|January 8, 2021
PubMed
Summary

This study introduces sequential semi-supervised segmentation (4S) for electron microscopy images. The 4S method effectively segments neural regions using limited labeled data and unlabeled data, outperforming traditional supervised learning.

Keywords:
Cell segmentationDeep neural networksPseudo-labelingSemi-supervised learning

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Image Analysis

Background:

  • Deep learning for electron microscopy image segmentation aids 3D reconstruction.
  • Generalization to new samples is challenging with rare samples or unstable scanning.

Purpose of the Study:

  • To develop a method for semi-automatic neural region extraction from electron microscopy image stacks.
  • To address limitations of generalization performance in transductive settings.

Main Methods:

  • Proposed sequential semi-supervised segmentation (4S) in a transductive setting.
  • Leveraged correlations between adjacent serial images.
  • Iterative training, inference, and pseudo-labeling with minimal teacher labels.

Main Results:

  • Demonstrated effectiveness in both quality and quantity on two types of serial section images.
  • Clarified the impact of teacher label quantity and position on segmentation performance.
  • Achieved superior performance compared to supervised learning with limited labeled data.

Conclusions:

  • 4S effectively extracts neural regions from serial image stacks using limited labeled and abundant unlabeled data in a transductive setting.
  • The method shows promise as a core component for future annotation tools.