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September 17, 2025

A U-Net Based Machine Learning Approach with Augmentation for Enhanced Precision in Multiple Sclerosis Lesion Segmentation from Multi-Modal MRI

Key Points

The U-Net based model achieved a Dice Similarity Coefficient score of 0.79 for MS lesion segmentation.
Automated segmentation of lesions from multi-modal MRI can enhance diagnostic precision in managing multiple sclerosis.
Our approach employs a deep convolutional neural network to streamline the tedious task of MS lesion identification.
Improved segmentation methods may lead to better patient outcomes and personalized treatment strategies through enhanced monitoring.

Abstract

Motivation: Early-stage diagnosis of Multiple Sclerosis (MS) is crucial for initiating prompt treatment. MRI can play a vital role in this process. Manual detection of MS lesions from MRI is time-consuming and subjective process, prone to human errors. Automated MS lesion segmentation techniques are needed; multi-modal MRI can be utilized for this purpose. Goal(s): The goal of our work was accurate determination of MS lesions from multi-modal MRI data. Approach: We developed a fully-automated approach to MS lesion segmentation using a U-Net based deep CNN. Results: Our model achieved a Dice Similarity Coefficient score of 0.79 for MS lesion segmentation from multi-modal MRI data. Impact: This study's robust MS lesion segmentation model could complement and improve diagnostic precision and monitoring for clinicians, leading to personalized treatment insights. It enables researchers to explore further multi-modal MRI benefits and model optimizations, ultimately enhancing patient care and outcomes.

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