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Immunogold Electron Microscopy

Immunogold Electron Microscopy

Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.

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Updated: May 12, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive

Shan Xiong¹, Jiabao Chen¹, Ye Wang²

¹College of Computer Science and Technology, Huaqiao University, Jimei Rd, Xiamen, 361021, Fujian, China.

Neuroinformatics

|May 11, 2026

Summary

This study introduces a weakly supervised domain adaptation method for segmenting mitochondria in electron microscopy images, significantly improving accuracy with minimal annotations. The approach enhances biological and neuroscience research by enabling efficient and precise cell structure analysis.

Keywords:

Contrastive learning Domain adaptive segmentation Electron microscopy Mitochondria segmentation Pseudo-labeling Weak supervision

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Last Updated: May 12, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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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:

Neuroscience
Cell Biology
Machine Learning

Background:

Accurate segmentation of mitochondria in electron microscopy (EM) images is crucial for biological and neuroscience research.
Unsupervised domain adaptation (UDA) methods face performance limitations in practical applications due to domain shifts.
Annotation costs for detailed segmentation in EM images are prohibitively high.

Purpose of the Study:

To develop an annotation-efficient segmentation method for mitochondria in EM images.
To investigate weakly supervised domain adaptation (WDA) using sparse point labels for improved accuracy.
To reduce the annotation effort and expert knowledge required for EM image segmentation.

Main Methods:

Introduced a multitask learning framework combining segmentation and center detection.
Employed a novel cross-teaching mechanism and class-focused cross-domain contrastive learning.
Implemented segmentation self-training with an instance-aware pseudo-label (IPL) selection strategy for reliable pseudo-labeling.

Main Results:

The proposed WDA method significantly outperforms existing UDA and WDA techniques on challenging datasets.
The method substantially narrows the performance gap compared to fully supervised approaches.
Achieved notable improvements over other UDA techniques even in an unsupervised setting.

Conclusions:

Weakly supervised domain adaptation with sparse point labels is a viable and effective strategy for mitochondria segmentation.
The proposed multitask learning and instance-aware pseudo-labeling approach enhances segmentation accuracy and efficiency.
This method offers a practical solution for large-scale EM image analysis in neuroscience and cell biology.