We previously showed that magnetic resonance fingerprinting (MRF) residual signals can be used for in vivo voxel-wise parcellation of the human cerebral cortex, using supervised machine learning classification algorithms. However, previous work relied on brain atlases to provide probabilistic masks of cortical region to label samples to train a classification model. Here, we investigate the feasibility of developing automated atlas-free cortical border delineation in individuals. We demonstrate that 90% of the cortical border voxels identified by the proposed framework are co-localised with the borders between two cortical areas on the Juelich maximum probability map of cerebral cortex in six participants.
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