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Related Experiment Video

Updated: Dec 31, 2025

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

Glomerulosclerosis identification in whole slide images using semantic segmentation.

Gloria Bueno¹, M Milagro Fernandez-Carrobles¹, Lucia Gonzalez-Lopez²

¹University of Castilla-La Mancha, ETSI Industriales, VISILAB, Ciudad Real, Spain.

Computer Methods and Programs in Biomedicine

|January 1, 2020

Summary

This study introduces a novel Convolutional Neural Network (CNN) approach for accurate glomeruli identification and classification in kidney pathology. The proposed SegNet-AlexNet model achieves high accuracy in detecting and categorizing normal versus sclerosed glomeruli from Whole Slide Imaging (WSI).

Keywords:

Consecutive segmentation-classification CNN Deep learning Digital pathology Glomeruli detection Sclerotic glomeruli Segnet Semantic segmentation U-Net

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Last Updated: Dec 31, 2025

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Published on: June 18, 2020

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Optical Clearing and Imaging of Immunolabeled Kidney Tissue

Optical Clearing and Imaging of Immunolabeled Kidney Tissue

Published on: July 22, 2019

Area of Science:

Nephropathology
Computational Pathology
Medical Image Analysis

Background:

Glomeruli identification is crucial for nephropathology studies.
Accurate detection and characterization of glomeruli are essential for diagnosing kidney diseases.

Purpose of the Study:

To propose a semantic segmentation and classification strategy using Convolutional Neural Networks (CNNs) for glomeruli detection and characterization.
To develop an automated method for distinguishing between normal and sclerosed glomeruli in Whole Slide Imaging (WSI).

Main Methods:

Comparison of U-Net and SegNet CNNs for pixel-level semantic segmentation of glomeruli.
Utilizing a two-class segmentation (glomerular vs. non-glomerular) followed by CNN classification.
Employing a fine-tuned AlexNet network for classifying segmented glomeruli as normal or sclerosed.

Main Results:

The SegNet-AlexNet consecutive CNN approach demonstrated superior performance.
Achieved 98.16% accuracy in glomeruli segmentation and classification.
Effectively reduced misclassified cases in glomerulosclerosis detection.

Conclusions:

The sequential CNN segmentation-classification strategy is highly accurate for glomerulosclerosis detection.
This methodology offers a reliable automated approach for analyzing kidney pathology slides.
The proposed method enhances diagnostic accuracy in nephropathology.