A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis.

ABSTRACT:

Background

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.

Methods

We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans

SUBMITTER: Lewis CJ 

PROVIDER: S-EPMC12015847 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature