Microstructure-informed brain segmentation in multiple sclerosis using CAQE

Motivation: Progression in neurodegenerative diseases such as multiple sclerosis (MS) involves tissue damage invisible on conventional MRI scans. Quantitative measures of tissue microstructure may be more informative. Goal(s): To segment MS brain MRI data based on quantitative microstructural MRI measures without spatial input. Approach: 23 MS brain scans were segmented based on clustering healthy quantitative data using an unsupervised Clustering for Anatomical Quantification and Evaluation (CAQE) framework. Classifications of lesions and normal appearing tissue were compared to a healthy atlas segmentation. Results: MS brains showed several differences from healthy classification in normal-appearing regions on conventional MRIs. Periventricular lesions were generally classified consistently. Impact: Using only microstructural features, the CAQE framework can classify diseased tissue in more detail than conventional segmentation algorithms based on qualitative MRI scans, and provide useful information for improved diagnosis, follow-up and more personalized care.