Abstract

Several neurological disorders are associated with hippocampal pathology. As changes may be localized to specific subfields or spanning across different subfields, accurate subfield segmentation may improve non-invasive diagnostics. We propose an automated subfield segmentation procedure, which combines surface-based processing with a patch-based template library and feature matching. Validation experiments in 25 healthy individuals showed high segmentation accuracy (Dice >82 % across all subfields) and robustness to variations in the template library size. Applying the algorithm to a cohort of patients with temporal lobe epilepsy and hippocampal sclerosis, we correctly lateralized the seizure focus in >90 %. This advantageously compares to classifiers relying on volumes retrieved from other state-of-the-art algorithms.