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

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 will release 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).

Does sleep disruption mediate the effects of childhood maltreatment on brain structure?

“…Volumetric segmentation was performed with the FreeSurfer image analysis suite Version 6.0, Technical details of these procedures are described in prior publications (Dale, Fischl, & Sereno, 1999; Fischl et al, 2004) including refined software for segmentation of the hippocampus (Iglesias et al, 2015). Overall, this approach provides hippocampal subfield volume measures that more closely align with histological measurements compared to the prior FreeSurfer release or alternative automated segmentation algorithms (Iglesias et al, 2015).…”

“…Overall, this approach provides hippocampal subfield volume measures that more closely align with histological measurements compared to the prior FreeSurfer release or alternative automated segmentation algorithms (Iglesias et al, 2015). …”

P2‐231: Systematic Comparison of Different Techniques to Measure Hippocampal Subfield Volumes in ADNI2

“…These structures were manually labeled in 15 ex vivo scans, and a probabilistic atlas of hippocampal anatomy was built from these delineations, in combination with manual segmentations of 39 1 mm T1 scans of the whole brain at the structure level (i.e., whole hippocampus, whole amygdala, etc.). These 39 scans are important to learn the defining image features of the anatomy of the brain structures surrounding the hippocampus (Iglesias et al, 2015).…”

“…Validation: Indirect validation by assessing the ability of the estimated subfield volumes to separate different groups (Iglesias et al, 2015).…”

“…This has been exploited by manual labeling approaches and increasingly also by automated approaches. Of the currently available approaches ASHS (Yushkevich et al, 2015b) and the latest version of Bayesian inference labeling implemented in Freesurfer 6.0 (Iglesias et al, 2015) were chosen for this project because both algorithms are publicly available and are used by the larger research community. The manual labeling protocol developed by Mueller et al (2007) was chosen as manual reference because its limited labeling scheme made it possible to label the over 100 images required for this project within a reasonable amount of time.…”