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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.
Purpose:
Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder characterized by progressive white matter demyelination. Quantification of demyelinated white matter on MRI-typically expressed as the demyelination load-serves as a key imaging biomarker of disease burden, enabling objective monitoring beyond visual rating scales. However, current semi-automated pipelines are limited by manual interaction, pediatric brain variability, and differences in MRI acquisition. This study aimed to develop and validate a self-configuring convolutional neural network (CNN) for automated segmentation of demyelinated white matter in MLD and to compare its performance with a conventional semi-automated method across heterogeneous MRI datasets.
Methods:
An nnU-Net was trained on 189 3D T1- and axial T2-weighted scans from 35 MLD patients using visually controlled conventional masks as ground truth. Independent testing was performed on 130 scans (73 high-resolution 3D, 57 lower-resolution 2D T1-weighted) from 49 patients. Performance was assessed by Dice coefficient, Bland-Altman bias, correlation with Gross Motor Function Classification (GMFC-MLD), MLD MRI severity score, longitudinal consistency, and qualitative review of outliers.
Results:
CNN-based segmentation showed strong spatial agreement with the reference method, with a median Dice coefficient of 0.82 for 3D T1-weighted scans and 0.75 for 2D scans. Volumetric bias was minimal on Bland-Altman analysis. CNN-derived demyelination load correlated significantly with motor impairment (rS = 0.38 for 3D and r = 0.56 for 2D; both p < 0.001) and showed a stronger association with the MLD MRI severity score than conventional segmentation (3D: rS = 0.48 vs. 0.28; 2D: rS = 0.83 vs. 0.29). Correlations with clinical status were slightly lower (CNN: rS = 0.38, p < 0.001; conventional: (rS = 0.26, p < 0.025)) Longitudinal analyses demonstrated stable, monotonic changes over time, and qualitative review revealed fewer boundary misclassifications.
Conclusion:
The nnU-Net enables fast, reproducible, and clinically meaningful segmentation of demyelinated white matter in MLD. It generalizes across MRI protocols, correlates with motor function, and offers a scalable tool for standardized biomarker extraction in clinical trials and other leukodystrophies.
Insights
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.
