Evaluating Traditional, Deep Learning, and Subfield Methods for Automatically Segmenting the Hippocampus from MRI

Abstract

Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning-based, and hippocampal subfield segmentation methods within a single investigation. We evaluated nine automatic hippocampal segmentation methods (FreeSurfer, FastSurfer, FIRST, e2dhipseg, HippMapper, Hippodeep, FreeSurfer-Subfields, HippUnfold and HSF) across three datasets with manually segmented hippocampus labels. Performance metrics included overlap with manual labels, correlations between manual and automatic volumes, diagnostic group differentiation, and systematically located false positives and negatives. Most methods, especially deep learning-based ones, performed well on public datasets but showed more error and variability on unseen data. Many methods tended to over-segment, particularly at the anterior hippocampus border, but were able to distinguish between healthy controls, MCI, and dementia patients based on hippocampal volume. Our findings highlight the challenges in hippocampal segmentation from MRI and the need for more publicly accessible datasets with manual labels across diverse ages and pathological conditions.