A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.
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).
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.
Juan Eugenio
JE
Iglesias
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Jean C
JC
Augustinack
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Khoa
K
Nguyen
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Christopher M
CM
Player
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Allison
A
Player
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Michelle
M
Wright
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Nicole
N
Roy
C.S. Kubik Laboratory for Neuropathology, Pathology Service, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Matthew P
MP
Frosch
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.
Ann C
AC
McKee
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Lawrence L
LL
Wald
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.
Bruce
B
Fischl
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.
Koen
K
Van Leemput
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis / Q000473:pathology; D001271:Atlases as Topic; D001921:Brain / Q000033:anatomy & histology / Q000473:pathology; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000473:pathology; D003937:Diagnosis, Differential; D005260:Female; D006624:Hippocampus / Q000033:anatomy & histology* / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged