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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
Computerised volumetric analysis of lesions in multiple sclerosis using new semi-automatic segmentation software
P Dastidar1, T Heinonen, T Vahvelainen
1Tampere University Hospital, Department of Diagnostic Radiology, Finland. prasun@koti.tpo.fi
Medical & Biological Engineering & Computing
|July 9, 1999
Summary
New semi-automatic software aids in detecting multiple sclerosis (MS) lesions and brain changes on MRI scans. Findings correlate MS plaque volume with clinical and neuropsychological deficits, aiding diagnosis.
Area of Science:
- Neuroimaging
- Medical Software Development
- Neurology
Background:
- Multiple Sclerosis (MS) diagnosis relies on Magnetic Resonance Imaging (MRI) for lesion detection and volume quantification.
- Understanding the correlation between MRI-derived parameters and clinical disability is crucial for MS patient management.
- Existing segmentation methods can be time-consuming and subjective.
Purpose of the Study:
- To evaluate a new semi-automatic segmentation software for detecting anatomical structures and lesions in secondary progressive MS patients.
- To investigate the correlation between MRI parameters (MS plaque volume, cerebrospinal fluid space volume) and clinical/neuropsychological deficits (EDSS, RFSS).
- To assess the quantitative accuracy and reproducibility of the segmentation software.
Main Methods:
- Application of semi-automatic segmentation software in 23 secondary progressive MS patients.
- Quantitative accuracy assessed using fluid-filled syringes (1.5% relative error).
- Reproducibility evaluated through intra- and inter-observer studies (3% and 7% variability).
- Correlation analysis between MRI volumes and clinical scores (EDSS, RFSS, neuropsychological deficits).
Main Results:
- Significant correlations found between MS plaque volumes and total RFSS scores (p=0.04).
- Relative intracranial CSF space volumes significantly correlated with EDSS scores (p=0.01).
- MS plaque volumes showed significant correlation with overall neuropsychological deficits (p=0.03).
- Three-dimensional (3D) visualization aided understanding of lesion-to-brain structure relationships.
Conclusions:
- Semi-automatic segmentation software demonstrates quantitative accuracy and reproducibility for MS lesion analysis.
- The study confirms significant correlations between MRI-derived volumes and clinical/neuropsychological deficits in MS.
- The use of semi-automatic segmentation techniques is recommended for clinical diagnosis and monitoring of MS patients.
