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

By Juan Eugenio Iglesias, Jean C. Augustinack, Khoa Nguyen, Christopher M. Player, Allison Player, M. Wright, N. Roy, Matthew P. Frosch, Ann C. McKee, Lawrence L. Wald and 2 more. NeuroImage, 2015

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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).

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/018b056424280ac4936e5988106dc17e26b1354f

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Citations in the corpus

High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas (2017, methodology): “Our previous work - the ex vivo hippocampal atlas (Iglesias et al., 2015) - uses a generative modeling framework to directly segment individual subject in vivo MRI data in target space; the resulting segmentation algorithm can be used to analyze standard in vivo MRI scans with varying overall image…”
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining (2024, positive): “Compared with transformers, Mamba scales linearly or near-linearly with sequence length while maintaining the capability of modeling long-range dependencies, offering higher accuracy, lower memory consumption, and less computational burden [33].”