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Related Experiment Video

Updated: Nov 7, 2025

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Multiple sclerosis lesion segmentation from brain MRI using U-Net based on wavelet pooling.

Ali Alijamaat¹, Alireza NikravanShalmani², Peyman Bayat¹

¹Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran.

International Journal of Computer Assisted Radiology and Surgery

|April 30, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep learning method using wavelet transform pooling for segmenting multiple sclerosis (MS) lesions in MRI scans, achieving superior accuracy for lesions of all sizes.

Keywords:

Deep learning Magnetic resonance imaging Multiple sclerosis U-Net neural network Wavelet

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Multiple sclerosis (MS) lesion segmentation in MRI is crucial for diagnosis and progression monitoring.
Accurate segmentation of lesions of varying sizes presents a significant challenge in automated analysis.
Current automated methods may struggle with the diverse size and shape characteristics of MS lesions.

Purpose of the Study:

To develop and evaluate an automated method for segmenting multiple sclerosis (MS) lesions in MRI images.
To improve the accuracy of MS lesion segmentation, particularly for lesions of different sizes.
To provide a tool that assists healthcare professionals in disease diagnosis and progression assessment.

Main Methods:

A deep neural network utilizing the U-Net architecture was developed.
Max pooling was replaced with a novel wavelet transform-based pooling mechanism.
Wavelet transform was applied for image decomposition, feature highlighting, and multi-resolution analysis.

Main Results:

The proposed wavelet transform-based pooling method demonstrated a superior Dice Similarity Coefficient (DSC) compared to traditional max pooling and average pooling.
The method showed improved performance in segmenting MS lesions across a range of sizes.
Quantitative results indicate enhanced segmentation accuracy over existing approaches.

Conclusions:

The novel U-Net architecture with wavelet transform pooling effectively segments multiple sclerosis lesions of various sizes in MRI scans.
This approach offers improved accuracy over standard max and average pooling techniques.
The method holds promise for enhancing the clinical utility of automated MS lesion segmentation.