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Related Concept Videos

Neurogenesis and Regeneration of Nervous Tissue01:15

Neurogenesis and Regeneration of Nervous Tissue

In the CNS, neurogenesis, the birth of new neurons from stem cells, is limited to the hippocampus in adults. In other regions of the brain and spinal cord, neurogenesis is almost non-existent due to inhibitory influences from neuroglia, especially oligodendrocytes, and the absence of growth-stimulating cues. The myelin produced by oligodendrocytes in the CNS inhibits neuronal regeneration. Furthermore, astrocytes proliferate rapidly after neuronal damage, forming scar tissue that physically...

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Investigating Mammalian Axon Regeneration: In Vivo Electroporation of Adult Mouse Dorsal Root Ganglion
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Deep learning-based hybrid analysis for axonal regeneration and myelination in rat sciatic nerve.

Jun Hong Won1, Chawon Yun1,2, So Young Lee1

  • 1Department of Orthopedic Surgery, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, Korea.

Scientific Reports
|July 10, 2026
PubMed
Summary

Automated analysis of peripheral nerve regeneration significantly speeds up histomorphometry. A hybrid deep learning approach offers the best balance of speed and accuracy for axon and myelin analysis.

Keywords:
Axon countsDeep learning segmentationHistometric analysisMyelinPeripheral nerveg-ratio

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Reproducible Mouse Sciatic Nerve Crush and Subsequent Assessment of Regeneration by Whole Mount Muscle Analysis
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Reproducible Mouse Sciatic Nerve Crush and Subsequent Assessment of Regeneration by Whole Mount Muscle Analysis
08:49

Reproducible Mouse Sciatic Nerve Crush and Subsequent Assessment of Regeneration by Whole Mount Muscle Analysis

Published on: February 22, 2012

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Quantitative histomorphometry of peripheral nerves is crucial for studying axonal regeneration and remyelination.
  • Manual analysis is time-consuming and not feasible for large-scale research.

Purpose of the Study:

  • To compare the efficiency and reliability of three automated morphometric methods against manual analysis for peripheral nerve studies.
  • To identify an optimal automated workflow for quantitative histomorphometry.

Main Methods:

  • Six rat sciatic nerves (3 naive, 3 regenerating) were analyzed using semithin transverse sections.
  • Manual analysis by three observers served as the reference.
  • Automated analyses included Trainable Weka Segmentation, AxonDeepSeg, and a refined AxonDeepSeg approach.
  • Key parameters: axon count, diameter, area, g-ratio, myelin thickness, and analysis time.

Main Results:

  • Automated methods reduced analysis time by over 50% compared to manual analysis.
  • AxonDeepSeg was the fastest automated method.
  • Fully automated methods showed variable agreement with manual analysis, especially in regenerating nerves.
  • A hybrid deep learning approach with manual refinement improved accuracy and reduced bias.

Conclusions:

  • Automated histomorphometry, particularly hybrid deep learning methods, offers a more efficient and reliable alternative to manual analysis for peripheral nerve studies.
  • This advancement supports large-scale investigations into axonal regeneration and remyelination.