An open-source tool for longitudinal whole-brain and white matter lesion segmentation

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.

Automated summary

In this paper, a longitudinal method for whole-brain segmentation of MRI scans is described and validated. The method extends an existing segmentation method to better track subtle morphological changes in neuroanatomical structures and white matter lesions. The proposed method is validated on datasets of control subjects and patients with Alzheimer's disease and multiple sclerosis and compared with other methods. The results show higher test-retest reliability and increased sensitivity to longitudinal disease effect differences.