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Multiple Sclerosis l: Introduction

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

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A Novel Convolutional Neural Network for Automated Multiple Sclerosis Brain Lesion Segmentation.

Emma Dereskewicz¹, Francesco La Rosa^1,2, Jonadab Dos Santos Silva¹

¹Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging

|September 12, 2025

Summary

FLAMeS, a deep learning algorithm, accurately segments multiple sclerosis (MS) brain lesions on MRI scans. This automated method outperforms existing tools, offering a faster and more consistent approach for MS research.

Keywords:

MRI lesion segmentation machine learning multiple sclerosis

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Area of Science:

Neuroimaging
Artificial Intelligence
Medical Image Analysis

Background:

Manual segmentation of multiple sclerosis (MS) brain lesions on MRI is labor-intensive and prone to variability.
Accurate lesion assessment is vital for MS research and clinical monitoring.

Purpose of the Study:

To develop and evaluate an automated algorithm for segmenting MS brain lesions using T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI.
To compare the performance of the developed algorithm against existing automated segmentation methods.

Main Methods:

Developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning algorithm utilizing the nnU-Net architecture.
Trained FLAMeS on 668 MS FLAIR MRI scans and validated on three independent datasets.
Assessed performance using qualitative expert review and quantitative segmentation metrics, comparing against SAMSEG, LST-LPA, and LST-AI.

Main Results:

FLAMeS was qualitatively preferred by blinded experts in 17 out of 20 scans.
Achieved a mean Dice score of 0.74, true positive rate of 0.84, and F1 score of 0.78 across testing datasets.
Outperformed benchmark methods, particularly in identifying smaller lesions, while missing fewer larger lesions.

Conclusions:

FLAMeS demonstrates high accuracy and robustness in segmenting MS brain lesions.
This automated deep learning approach surpasses current publicly available methods for MS lesion segmentation.