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Updated: Jun 28, 2025

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

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Published on: June 18, 2020

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal

Chia-Feng Juang¹, Ya-Wen Chuang², Guan-Wen Lin¹

¹Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|April 10, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning method for automated glomerulus detection and classification in kidney pathology images. The approach enhances diagnostic efficiency and accuracy for renal pathologists.

Keywords:

Deep learning Generative adversarial network Glomerulus classification Glomerulus detection Glomerulus segmentation Renal pathology images

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Last Updated: Jun 28, 2025

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An Efficient Sieving Method to Isolate Intact Glomeruli from Adult Rat Kidney

An Efficient Sieving Method to Isolate Intact Glomeruli from Adult Rat Kidney

Published on: November 1, 2018

Area of Science:

Nephrology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Glomerulus morphology in renal pathology images is crucial for diagnosis and prognosis.
Current manual interpretation is time-consuming and labor-intensive, necessitating efficient, standardized methods.

Purpose of the Study:

To develop and validate a deep convolutional neural network (CNN)-based approach for automated glomerulus detection and classification.
To improve the efficiency and accuracy of interpreting renal pathology images.

Main Methods:

A flattened Xception with a feature pyramid network (FX-FPN) was used for glomerulus detection within a Faster R-CNN framework.
A flattened Xception classifier was employed for classifying five glomerulus morphologies.
Cycle-consistent generative adversarial networks (CycleGAN) were utilized for generative data augmentation of glomerulus patches.

Main Results:

The detection model achieved an F1 score of 0.9524 for H&E and PAS stains.
Classification sensitivity and specificity were 0.7077 and 0.9316, respectively, with original data.
Generative data augmentation improved sensitivity to 0.7623 and specificity to 0.9443.

Conclusions:

The proposed deep CNN approach effectively detects and classifies glomeruli in renal pathology images.
Generative data augmentation significantly enhances classification performance, offering a superior alternative to manual interpretation.