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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Glomerulus Detection Using Segmentation Neural Networks.

Surender Singh Samant¹, Arun Chauhan², Jagadish Dn³

¹Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, Uttarkhand, India. surender.samant@gmail.com.

Journal of Digital Imaging

|April 5, 2023

Summary

This study introduces a deep learning method for detecting glomeruli in kidney tissue images, crucial for diagnosing kidney diseases. The approach achieved high accuracy using convolutional neural networks on digitized kidney slides.

Keywords:

Convolutional neural networks Glomerulus detection Kidney biopsy Kidney transplantation Semantic segmentation

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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

Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Accurate kidney disease diagnosis and pre-transplant assessment rely on digital pathology.
Glomerulus detection in kidney tissue is a critical but challenging step in pathological analysis.

Purpose of the Study:

To develop and evaluate a deep learning-based method for automated glomerulus detection in digitized kidney tissue segments.
To improve the efficiency and accuracy of glomerulus identification for kidney disease diagnosis.

Main Methods:

Utilized deep learning models, specifically convolutional neural networks (CNNs).
Employed various CNN architectures including ResNets, UNet, LinkNet, and EfficientNet for training.
Tested the models on the NIH HuBMAP kidney whole slide image dataset.

Main Results:

The proposed deep learning method demonstrated high performance in glomerulus detection.
Achieved a Dice coefficient of 0.942, indicating excellent segmentation accuracy.
Various CNN architectures were explored, with the best-performing model selected.

Conclusions:

Deep learning offers a powerful solution for automated glomerulus detection in digital pathology.
The developed method significantly advances the capabilities for kidney disease diagnosis and transplantation evaluation.
High accuracy in glomerulus detection is achievable with advanced CNN models.