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Related Experiment Video

Updated: Apr 6, 2026

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

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

Published on: April 14, 2014

A toolbox for multiple sclerosis lesion segmentation.

Eloy Roura¹, Arnau Oliver², Mariano Cabezas³

¹Computer Vision and Robotics Group, University of Girona, Campus Montilivi, Ed. P-IV, 17071, Girona, Spain. eloyroura@eia.udg.edu.

|August 1, 2015

Summary

This summary is machine-generated.

This study introduces an automated tool for segmenting multiple sclerosis (MS) brain lesions using T1w and FLAIR MRI scans, improving diagnostic efficiency and accuracy.

Keywords:

Automated tool Lesion detection Lesion segmentation Magnetic resonance images Multiple sclerosis

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Last Updated: Apr 6, 2026

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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 analysis
Neurology
Artificial intelligence in healthcare

Background:

Accurate lesion segmentation is crucial for multiple sclerosis (MS) diagnosis and monitoring.
Manual segmentation is time-consuming and prone to variability.
Automated tools are needed to improve efficiency and consistency.

Purpose of the Study:

To develop and present a novel automated tool for MS lesion segmentation.
To utilize both T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) MRI sequences.
To address the limitations of manual segmentation methods.

Main Methods:

The approach involves initial brain tissue segmentation (gray matter, white matter, cerebrospinal fluid) from T1w images.
Lesions are subsequently identified as outliers in normal-appearing gray matter on FLAIR images.
The method leverages established neuroimaging software (SPM8/12).

Main Results:

The tool demonstrated superior precision in lesion segmentation compared to existing methods.
Sensitivity and Dice similarity measures were comparable to other approaches.
Validation was performed on data from over 100 MS patients across diverse scanner types and field strengths.

Conclusions:

The developed tool offers an automated solution for MS lesion segmentation.
It is available as a public extension for SPM8/12, benefiting both clinical and research settings.
This tool has the potential to streamline MS patient management and research.