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Related Experiment Video

Updated: Feb 5, 2026

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis

Published on: July 19, 2019

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Olivier Commowick¹, Audrey Istace², Michaël Kain³

¹VISAGES: INSERM U1228 - CNRS UMR6074 - Inria, University of Rennes I, Rennes, France. Olivier.Commowick@inria.fr.

Scientific Reports

|September 14, 2018

Summary

This summary is machine-generated.

This study evaluated multiple sclerosis (MS) segmentation algorithms using an open-science infrastructure. Automatic methods, including machine learning, still lag behind human experts in lesion detection and delineation.

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Published on: December 26, 2015

Area of Science:

Medical image analysis
Computational neuroscience
Artificial intelligence in healthcare

Background:

Multiple sclerosis (MS) lesion segmentation is crucial for disease monitoring.
Evaluating segmentation algorithms requires standardized, reproducible methods.
Previous challenges lacked comprehensive, automated evaluation frameworks.

Purpose of the Study:

To assess the performance of various multiple sclerosis lesion segmentation algorithms.
To compare automated algorithm performance against human expert segmentation.
To introduce and utilize a novel open-science computing infrastructure for algorithm evaluation.

Main Methods:

Utilized the international MICCAI 2016 challenge framework with a new open-science computing infrastructure.
Evaluated thirteen state-of-the-art multiple sclerosis segmentation algorithms.
Used a high-quality database of 53 MS cases from four centers, manually annotated by seven experts.

Main Results:

Automatic segmentation algorithms, including machine learning (random forests, deep learning), underperformed human experts in lesion detection and delineation.
A consensus of automated algorithms showed performance closer to human expertise for segmentation accuracy.
Detection scores for consensus algorithms remained below human expert levels.

Conclusions:

Current automated multiple sclerosis segmentation algorithms have not yet surpassed human expert performance.
Open-science computing infrastructure enables fair and automatic evaluation of segmentation algorithms.
Further development is needed to improve automated detection and delineation of MS lesions.