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Updated: Jul 25, 2026
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
Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features
M Rincón1, E Díaz-López2, P Selnes3
1Department of Artificial Intelligence, UNED, Madrid, Spain. mrincon@dia.uned.es.
Abstract:
Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular and neurodegenerative diseases among the elderly. Consequently, detection and characterization of WMHs are of significant clinical importance. We propose a novel approach for WMH segmentation from multi-contrast MRI where both voxel-based and lesion-based information are used to improve overall performance in both volume-oriented and object-oriented metrics. Our segmentation method (AMOS-2D) consists of four stages following a "generate-and-test" approach: pre-processing, Gaussian white matter (WM) modelling, hierarchical multi-threshold WMH segmentation and object-based WMH filtering using support vector machines. Data from 28 subjects was used in this study covering a wide range of lesion loads. Volumetric T1-weighted images and 2D fluid attenuated inversion recovery (FLAIR) images were used as basis for the WM model and lesion masks defined manually in each subject by experts were used for training and evaluating the proposed method. The method obtained an average agreement (in terms of the Dice similarity coefficient, DSC) with experts equivalent to inter-expert agreement both in terms of WMH number (DSC = 0.637 vs. 0.651) and volume (DSC = 0.743 vs. 0.781). It allowed higher accuracy in detecting WMH compared to alternative methods tested and was further found to be insensitive to WMH lesion burden. Good agreement with expert annotations combined with stable performance largely independent of lesion burden suggests that AMOS-2D will be a valuable tool for fully automated WMH segmentation in patients with cerebrovascular and neurodegenerative pathologies.
Insights
This study introduces AMOS-2D, a new method for segmenting white matter hyperintensities (WMHs) in brain MRI scans. The approach accurately identifies WMHs, matching expert performance across various lesion loads.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Neuroscience
Background:
- Brain white matter hyperintensities (WMHs) are crucial indicators of cerebrovascular and neurodegenerative diseases in the elderly.
- Accurate detection and characterization of WMHs are vital for clinical assessment and disease management.
Purpose of the Study:
- To develop and evaluate a novel, automated approach for segmenting white matter hyperintensities (WMHs) from multi-contrast MRI data.
- To improve segmentation performance using both voxel-based and lesion-based information for enhanced accuracy in volume and object metrics.
Main Methods:
- The proposed AMOS-2D method employs a four-stage "generate-and-test" strategy: pre-processing, Gaussian white matter (WM) modeling, hierarchical multi-threshold WMH segmentation, and support vector machine-based object filtering.
- Utilized volumetric T1-weighted and 2D FLAIR MRI sequences from 28 subjects with diverse WMH loads.
- Expert-defined manual lesion masks served as the ground truth for training and validation.
Main Results:
- The AMOS-2D method achieved performance comparable to inter-expert agreement for both WMH number (Dice Similarity Coefficient [DSC] = 0.637) and volume (DSC = 0.743).
- Demonstrated superior accuracy in WMH detection compared to alternative methods.
- Exhibited consistent performance across a wide range of WMH lesion burdens, indicating robustness.
Conclusions:
- The AMOS-2D approach provides a valuable tool for fully automated WMH segmentation in clinical settings.
- Its high agreement with expert annotations and stability across varying lesion loads support its utility for patients with cerebrovascular and neurodegenerative diseases.
