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November 8, 2025Open Access

Deep Learning Enables Automated Segmentation and Quantification of Ultrastructure from Transmission Electron Microscopy Images

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Authors

AZAnruo ZouWTWei Yap TanJJJiayi Ji

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Overview

Automated segmentation and quantification of glomerular basement membrane widths shows promise for nephrology research and clinical trials, implying improved diagnosis accuracy.

Key Points

  • Automated deep learning framework enhances the measurement of kidney ultrastructure, leading to more precise assessments.
  • The method shows high concordance with expert annotations while significantly reducing measurement time and improving reproducibility.
  • Assessment includes widths of kidney glomerular basement membrane and podocyte foot processes across species, including mouse, rat, and human.
  • Highlighting the efficacy of a digital pathology solution, this approach supports both research and clinical applications in nephrology.

Cite This Study

Zou et al. (2025) studied this question.

synapsesocial.com/papers/690e8b6ca5b062d7a4e734b6https://doi.org/10.1101/2025.11.05.686793
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Also Consider

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  1. 1Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis2025
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  3. 3Glomerular Structure and Function in Type 2 Diabetes Stratified by Glomerular Basement Membrane Thickening2026
  4. 4Medical Segmentation of Kidney Whole Slide Images Using Slicing Aided Hyper Inference and Enhanced Syncretic Mask Merging Optimized by Particle Swarm Metaheuristics2025
  5. 5Clinical Relevance of Computationally Derived Attributes of Arteries and Arterioles in focal segmental glomerulosclerosis and minimal change disease2025