A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI

TLDR

Current neuroimaging studies lack high‑resolution computational atlases needed for automated subregional hippocampal MRI analysis. The study aims to construct a statistical atlas of hippocampal subregions using ultra‑high resolution ex vivo MRI. The atlas was built from 15 autopsy samples scanned at 0.13 mm isotropic resolution, manually segmented into 13 substructures, combined with in vivo T1‑weighted scans, and assembled using a Bayesian inference algorithm to produce a multimodal, adaptive segmentation tool. Experiments on three public datasets show the atlas and segmentation method can segment T1, T2, and combined images, replicate mild cognitive impairment findings from high‑resolution T2 data, and distinguish Alzheimer’s patients from controls with 88 % accuracy in 1 mm T1 scans, outperforming FreeSurfer 5.3 and whole‑hippocampal‑volume classification.

Abstract

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1 mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1 mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy).