Clustering for Anatomical Quantification and Evaluation (CAQE): Unsupervised brain segmentation with quantitative MRI

Motivation: Traditional image segmentation uses conventional MRI to classify tissue types based on image intensity. However, segmenting using microstructural data may provide more specific classification of tissue. Goal(s): Cluster and segment brain tissue using quantitative MRI measures, to classify tissue based only on microstructural features without spatial input. Approach: Measures denoting myelin content, anisotropy and tissue heterogeneity were clustered and used to label test datasets based on microstructural features alone, using an unsupervised approach. Results: Segmentations were more informative than traditional segmentation, and consistent between healthy subjects. Differences between clusters reflect microstructural feature differences which would otherwise be invisible with conventional imaging. Impact: The CAQE framework can be used for segmentation of tissue based on quantitative measures alone, providing better delineation of regions based on microstructural features. This allows for future comparisons between healthy and damaged tissue, to visualise and interpret pathological changes.