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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
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Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis
Michael Platten1,2,3, Irene Brusini2,4, Olle Andersson2
1Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
Summary
Deep learning accurately measures corpus callosum atrophy in multiple sclerosis (MS) patients. This tool, DeepnCCA, shows stronger correlations with disability than FreeSurfer, aiding disease monitoring.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Neurology
Background:
- Corpus callosum atrophy is a key biomarker for multiple sclerosis (MS) neurodegeneration.
- Traditional segmentation methods are manual and time-consuming.
- Need for automated, accurate segmentation for large-scale studies.
Purpose of the Study:
- Develop a supervised machine learning algorithm (DeepnCCA) for corpus callosum segmentation.
- Relate corpus callosum morphology to clinical disability in MS patients.
- Utilize conventional MRI scans for routine clinical application.
Main Methods:
- Developed and validated DeepnCCA using 200 2D T2-weighted MRI scans from 553 MS patients.
- Compared DeepnCCA segmentations with FreeSurfer on 504 3D T1-weighted scans.
- Correlated segmentation outputs with clinical disability measures (EDSS, Symbol Digit Modalities Test).
Main Results:
- DeepnCCA demonstrated high segmentation accuracy (Dice coefficients 98.1% and 89.3%).
- DeepnCCA showed stronger correlations with cognitive and physical disability compared to FreeSurfer.
- Corpus callosum thinning correlated with increased disability; increased physical disability correlated with a more angled corpus callosum.
Conclusions:
- DeepnCCA is an open-source tool for fast and accurate corpus callosum measurements in MS.
- The tool is suitable for large MS cohorts and monitoring disease progression.
- Potential for tracking therapy response in multiple sclerosis patients.
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