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Glomerular Filtration

Glomerular Filtration

The filtration membrane in the renal system is a highly specialized structure essential for filtering blood. It consists of glomerular capillaries and podocytes, forming a selective barrier that permits the passage of water and small solutes while restricting most plasma proteins and blood cells.
Components of the Filtration Membrane
The filtration process involves three key layers: the glomerular endothelial cells, the basement membrane, and the podocyte-formed filtration slits.

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Related Experiment Video

Updated: May 5, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

GloFinder: AI-empowered QuPath plugin for WSI-level glomerular detection, visualization, and curation.

Jialin Yue¹, Tianyuan Yao², Ruining Deng²

¹Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.

Journal of Pathology Informatics

|April 7, 2025

Summary

This summary is machine-generated.

GloFinder, a new QuPath plugin, automates kidney glomeruli detection in whole slide images using AI. This tool enhances accessibility for clinicians by offering single-click analysis and user-friendly editing capabilities.

Keywords:

Automated glomerular detection CircleNet Medical image analysis QuPath plugin Renal pathology Weighted circle fusion Whole slide images

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Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues

Published on: February 9, 2024

Area of Science:

Nephropathology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Automated detection of kidney glomeruli in whole slide images (WSIs) is crucial for pathological analysis.
Existing AI tools often require programming expertise, limiting accessibility for clinicians.
Current models may lack flexibility and are typically trained on limited datasets.

Purpose of the Study:

To develop an accessible, user-friendly AI tool for automated glomerular detection in WSIs.
To improve the accuracy and flexibility of AI-driven glomerular detection in kidney pathology.
To facilitate seamless integration into clinical workflows for nephropathology research and practice.

Main Methods:

Introduction of GloFinder, a QuPath plugin for single-click automated glomerular detection.
Utilization of CircleNet, an anchor-free detection framework with circle representations.
Implementation of weighted circle fusion, an ensemble method combining multiple CircleNet models for refined predictions.
Training on approximately 160,000 manually annotated glomeruli.

Main Results:

GloFinder provides single-click automated detection of glomeruli across entire WSIs.
The plugin enables online editing and direct visualization of results within the QuPath graphical user interface.
Weighted circle fusion enhances prediction accuracy through ensemble modeling.
Achieved superior performance in glomerular detection compared to existing methods.

Conclusions:

GloFinder offers a powerful and accessible solution for automated glomerular detection in kidney pathology.
The plugin streamlines analysis for clinicians and researchers, improving efficiency in nephropathology.
Enhanced user interaction and accuracy make GloFinder a valuable tool for both research and clinical practice.