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Updated: Sep 27, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using

Jincheng Wang¹, Sijie Chen², Hui Liang³

¹Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Frontiers in Neurology

|April 11, 2022

Summary

A new automated model using linear measurements on CT scans effectively classifies brain atrophy in cerebral small vascular disease (CSVD) patients. This method shows comparable performance to deep learning models but offers improved interpretability for clinical use.

Keywords:

automated classification brain atrophy cerebral small vessel disease computed tomography convolutional neural networks deep learning linear measurement

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Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases

Longitudinal In Vivo Imaging of the Cerebrovasculature: Relevance to CNS Diseases

Published on: December 6, 2016

Area of Science:

Medical Imaging
Neurology
Artificial Intelligence in Medicine

Background:

Brain atrophy is a key indicator of cerebral small vascular disease (CSVD).
Accurate classification of brain atrophy is crucial for managing CSVD.
Current methods may lack efficiency or interpretability.

Purpose of the Study:

To develop an automated brain atrophy classification model using linear measurements on CT images for CSVD patients.
To compare the performance of this linear-measurement-based model with an end-to-end Convolutional Neural Networks (CNNs) model.
To evaluate the impact of integrating patient age and gender on classification accuracy.

Main Methods:

A dataset of 385 subjects (no atrophy, mild atrophy, severe atrophy) was used.
A 2D model based on manual annotations of nine linear measurements and sulci widening was created.
An end-to-end 3D deep learning (CNN) model was also developed for comparison.
Both models were evaluated with and without patient age and gender data.

Main Results:

Automated linear measurements showed good agreement with manual annotations.
The 2D model achieved an AUC of 0.953 for two-type classification, comparable to the 3D model (0.941).
The 2D model's weighted kappa for three-type classification (0.727) outperformed the 3D model (0.607).
Integrating age and gender improved classification performance for both models.

Conclusions:

An automated linear measurement-based model can effectively classify CSVD-related brain atrophy on CT images.
This 2D model offers similar performance to 3D CNNs but with enhanced interpretability.
The proposed model shows potential for advantageous application in clinical settings.