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Related Experiment Video

Updated: Apr 24, 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

BOOST: a supervised approach for multiple sclerosis lesion segmentation.

Mariano Cabezas¹, Arnau Oliver², Sergi Valverde²

¹Department of Computer Architecture and Technology, University of Girona, Spain; Magnetic Resonance Unit, Department of Radiology, Vall d'Hebron University Hospital, Spain.

Journal of Neuroscience Methods

|September 8, 2014

Summary

This summary is machine-generated.

BOOST, a new knowledge-based method, automatically segments multiple sclerosis lesions. It shows competitive results and better overlap with manual annotations, especially for high lesion loads.

Keywords:

Artificial intelligence Brain analysis Image analysis Magnetic resonance imaging Multiple sclerosis

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Last Updated: Apr 24, 2026

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Published on: September 25, 2019

Area of Science:

Medical image analysis
Neurology
Artificial intelligence in healthcare

Background:

Automatic segmentation of multiple sclerosis lesions is complex.
Recent techniques benefit from prior knowledge and contextual information.

Purpose of the Study:

To present BOOST, a novel knowledge-based approach for automated multiple sclerosis lesion segmentation.
To evaluate its performance against existing methods and manual annotations.

Main Methods:

Utilized a voxel-by-voxel classification approach named BOOST.
Employed the Gentleboost classifier with features including contextual information, registered atlas probability maps, and an outlier map.

Main Results:

Evaluated on 45 cases from three hospitals.
Achieved moderate agreement between automated segmentation and manual annotations.
Demonstrated competitive results and improved overlap compared to state-of-the-art methods.

Conclusions:

BOOST shows potential for clinical application in multiple sclerosis lesion segmentation.
The method performs better on cases with high lesion load.
Further improvements are needed for accurate segmentation of small lesion loads.