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Related Experiment Video

Updated: Mar 2, 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

Longitudinal multiple sclerosis lesion segmentation data resource.

Aaron Carass^1,2, Snehashis Roy³, Amod Jog²

¹Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.

|May 12, 2017

Summary

This summary is machine-generated.

This study provides a valuable dataset for longitudinal multiple sclerosis (MS) lesion segmentation, featuring expert-annotated multi-modal scans for training and testing automated analysis methods.

Keywords:

Magnetic resonance imaging Multiple sclerosis

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Last Updated: Mar 2, 2026

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Area of Science:

Biomedical Imaging
Neurology
Medical Image Analysis

Background:

Longitudinal data is crucial for understanding disease progression in multiple sclerosis (MS).
Accurate segmentation of MS lesions over time is essential for clinical assessment and treatment monitoring.
Automated methods require well-curated datasets for development and validation.

Purpose of the Study:

To establish a comprehensive dataset for longitudinal multiple sclerosis lesion segmentation.
To facilitate the development and evaluation of automated lesion segmentation algorithms.
To support research presented at the 2015 International Symposium on Biomedical Imaging.

Main Methods:

Organized a longitudinal MS lesion segmentation challenge with training and test data.
Collected multi-modal MRI scans from 19 subjects across multiple time-points.
Expert raters manually delineated white matter lesions associated with MS in all 82 datasets.

Main Results:

Provided training data from 5 subjects (mean 4.4 time-points) and test data from 14 subjects (mean 4.4 time-points).
All 82 datasets were annotated by two expert raters for MS white matter lesions.
Training data with multi-modal scans and lesion masks is available for download.

Conclusions:

The released dataset serves as a valuable resource for the biomedical imaging and neurology research communities.
Availability of training and testing data enables robust evaluation of automated longitudinal MS lesion segmentation techniques.
This resource supports advancements in the analysis of multiple sclerosis progression using medical imaging.