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Related Experiment Video

Updated: Mar 8, 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: Resource and challenge.

Aaron Carass¹, Snehashis Roy², Amod Jog³

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

|January 15, 2017

Summary

This summary is machine-generated.

This study presents a lesion segmentation challenge for multiple sclerosis, offering a valuable dataset for researchers. It evaluates algorithms and refines metrics, enhancing future studies in the field.

Keywords:

Magnetic resonance imaging Multiple sclerosis

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Last Updated: Mar 8, 2026

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Area of Science:

Medical Image Analysis
Neurology
Computational Biology

Background:

Multiple Sclerosis (MS) lesion segmentation is crucial for disease monitoring.
Existing lesion segmentation methods require robust evaluation and comparison.
Longitudinal data is essential for understanding MS progression.

Purpose of the Study:

To organize a longitudinal lesion segmentation challenge.
To provide a standardized dataset for evaluating segmentation algorithms.
To compare the performance of various lesion segmentation techniques.

Main Methods:

Organized a challenge with training and test datasets of MS brain scans.
Collected data from 5 training subjects and 14 test subjects, with multiple time-points.
Eleven teams submitted results from state-of-the-art lesion segmentation algorithms.

Main Results:

Quantitative evaluation of eleven submitted algorithms and three additional methods.
Comparison of algorithm performance against two expert human raters.
Analysis of inter-rater consistency and development of a consensus delineation.

Conclusions:

The challenge facilitated data sharing and community collaboration in MS lesion segmentation.
Performance evaluation and metric refinement were key outcomes.
The provided dataset serves as a valuable resource for future research and method comparison.