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Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy
Pascal Martin1, Joël Schaerer2, Thomas Cajgfinger2
1Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany. pascal.martin@med.uni-tuebingen.de.
Clinical Neuroradiology
|April 21, 2026
Summary
A novel convolutional neural network (CNN) accurately segments demyelinated white matter in Metachromatic Leukodystrophy (MLD). This automated method improves disease burden quantification and monitoring compared to traditional approaches.
Area of Science:
- Neuroimaging
- Artificial Intelligence in Medicine
- Neurology
Background:
- Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder causing progressive white matter demyelination.
- Demyelination load on MRI is a key biomarker for disease burden but current methods are limited by manual interaction and MRI variability.
- Automated segmentation tools are needed to overcome limitations of current semi-automated pipelines.
Purpose of the Study:
- Develop and validate a self-configuring convolutional neural network (CNN) for automated demyelinated white matter segmentation in MLD.
- Compare the CNN's performance against a conventional semi-automated method across diverse MRI datasets.
- Assess the CNN's utility as a scalable tool for biomarker extraction in MLD.
Main Methods:
- An nnU-Net model was trained using 189 3D T1- and axial T2-weighted MRI scans from 35 MLD patients.
- Independent testing involved 130 scans from 49 patients, including high-resolution 3D and lower-resolution 2D T1-weighted images.
- Performance was evaluated using Dice coefficient, Bland-Altman analysis, correlation with clinical scores (GMFC-MLD, MLD MRI severity), and longitudinal consistency.
Main Results:
- The CNN achieved strong spatial agreement with reference segmentations (median Dice: 0.82 for 3D, 0.75 for 2D T1w scans).
- CNN-derived demyelination load showed significant correlations with motor impairment and MLD MRI severity, outperforming conventional methods.
- The CNN demonstrated robust performance across different MRI protocols and longitudinal stability.
Conclusions:
- The nnU-Net provides fast, reproducible, and clinically meaningful segmentation of demyelinated white matter in MLD.
- This automated approach generalizes across MRI protocols and offers a scalable solution for standardized biomarker extraction.
- The CNN is a valuable tool for monitoring disease progression and evaluating treatment efficacy in MLD and other leukodystrophies.
