Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.

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.