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

Juan Eugenio Iglesias; Jean C Augustinack; Khoa Nguyen; Christopher M Player; Allison Player; Michelle Wright; Nicole Roy; Matthew P Frosch; Ann C McKee; Lawrence L Wald; Bruce Fischl; Koen Van Leemput; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.neuroimage.2015.04.042

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

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

Juan Eugenio Iglesias, Jean C Augustinack, Khoa Nguyen, Christopher M Player, Allison Player, Michelle Wright, Nicole Roy, Matthew P Frosch, Ann C McKee, Lawrence L Wald, …

NeuroImage (Orlando, Fla.), Vol.115, pp.117-137

07/15/2015

DOI: 10.1016/j.neuroimage.2015.04.042

PMCID: PMC4461537

PMID: 25936807

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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 (1mm 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 (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and classification based on whole hippocampal volume (82% accuracy).

Algorithms

Diagnosis, Differential

Brain - anatomy & histology

Atlases as Topic

Humans

Middle Aged

Magnetic Resonance Imaging - methods

Male

Hippocampus - pathology

Cognitive Dysfunction - pathology

Image Processing, Computer-Assisted - methods

Alzheimer Disease - pathology

Alzheimer Disease - diagnosis

Aged, 80 and over

Brain - pathology

Female

Aged

Cognitive Dysfunction - diagnosis

Hippocampus - anatomy & histology

Details

Title: Subtitle: A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI
Creators: Juan Eugenio Iglesias - Basque Center on Cognition, Brain and Language, San Sebastián, Spain; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: e.iglesias@bcbl.eu
Jean C Augustinack - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Khoa Nguyen - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Christopher M Player - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Allison Player - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Michelle Wright - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Nicole Roy - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Matthew P Frosch - C.S. Kubik Laboratory for Neuropathology, Pathology Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Ann C McKee - Department of Neurology, Boston University, School of Medicine, Boston, MA, USA; Department of Pathology, Boston University, School of Medicine, Boston, MA, USA; United States Department of Veterans Affairs, VA Boston Healthcare System, Boston, MA, USA; Bedford Veterans Administration Medical Center, Bedford, MA, USA
Lawrence L Wald - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Bruce Fischl - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and AI lab, Massachusetts Institute of Technology, Cambridge, MA, USA
Koen Van Leemput - Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Department of Information and Computer Science, Aalto University, Finland; Department of Biomedical Engineering and Computational Science, Aalto University, Finland
Alzheimer's Disease Neuroimaging Initiative
Contributors: Laura L Boles-Ponto (Contributor) - University of Iowa, Radiology
Resource Type: Journal article
Publication Details: NeuroImage (Orlando, Fla.), Vol.115, pp.117-137
DOI: 10.1016/j.neuroimage.2015.04.042
PMID: 25936807
PMCID: PMC4461537
NLM abbreviation: Neuroimage
ISSN: 1053-8119
eISSN: 1095-9572
Publisher: United States
Grant note: P30 AG062421 / NIA NIH HHS AG022381 / NIA NIH HHS 1R21NS072652-01 / NINDS NIH HHS RC1 AT005728 / NCCIH NIH HHS R01 NS083534 / NINDS NIH HHS R01 AG022381 / NIA NIH HHS P41 EB015896 / NIBIB NIH HHS 5R01AG008122-22 / NIA NIH HHS K01AG028521 / NIA NIH HHS U01 AG024904 / NIA NIH HHS U01 MH093765 / NIMH NIH HHS 1S10RR023043 / NCRR NIH HHS K01-AG030514 / NIA NIH HHS P41EB015896 / NIBIB NIH HHS R01 NS052585 / NINDS NIH HHS R01 NS052585-01 / NINDS NIH HHS S10 RR023043 / NCRR NIH HHS P30 AG010129 / NIA NIH HHS S10 RR023401 / NCRR NIH HHS R01 AG016495 / NIA NIH HHS 1S10RR019307 / NCRR NIH HHS RC1 AT005728-01 / NCCIH NIH HHS R01 AG008122 / NIA NIH HHS R01NS083534 / NINDS NIH HHS P30 AG013846 / NIA NIH HHS P30-AG010129 / NIA NIH HHS S10 RR019307 / NCRR NIH HHS 1S10RR023401 / NCRR NIH HHS K01 AG030514 / NIA NIH HHS R01EB006758 / NIBIB NIH HHS U24 RR021382 / NCRR NIH HHS P50 AG005134 / NIA NIH HHS R01EB013565 / NIBIB NIH HHS P30AG13846 / NIA NIH HHS K01 AG028521 / NIA NIH HHS R21 NS072652 / NINDS NIH HHS R01 EB006758 / NIBIB NIH HHS 5U01-MH093765 / NIMH NIH HHS R01 EB013565 / NIBIB NIH HHS R01 NS070963 / NINDS NIH HHS R01AG1649 / NIA NIH HHS 1R01NS070963 / NINDS NIH HHS
Language: English
Date published: 07/15/2015
Academic Unit: Radiology; Pharmaceutical Sciences and Experimental Therapeutics
Record Identifier: 9984051722602771

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