An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
This work provides an incremental improvement for researchers and clinicians needing more reliable and sensitive tools to monitor brain changes over time in neurodegenerative diseases.
The paper tackles the problem of tracking subtle morphological changes in neuroanatomical structures and white matter lesions from longitudinal MRI scans by extending an existing segmentation method with subject-specific latent variables for temporal consistency. The results show that the method achieves higher test-retest reliability and greater sensitivity to longitudinal disease effects in Alzheimer's disease and multiple sclerosis patients compared to cross-sectional and benchmark longitudinal methods.
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer.