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

被引:917
作者
Eugenio Iglesias, Juan [1 ,2 ]
Augustinack, Jean C. [2 ]
Khoa Nguyen [2 ]
Player, Christopher M. [2 ]
Player, Allison [2 ]
Wright, Michelle [2 ]
Roy, Nicole [2 ]
Frosch, Matthew P. [3 ]
McKee, Ann C. [4 ,5 ,6 ,7 ]
Wald, Lawrence L. [2 ]
Fischl, Bruce [2 ,8 ]
Van Leemput, Koen [2 ,9 ,10 ,11 ]
机构
[1] Basque Ctr Cognit Brain & Language, San Sebastian, Spain
[2] Harvard Univ, Massachusetts Gen Hosp, Sch Med, Martinos Ctr Biomed Imaging, Boston, MA USA
[3] Harvard Univ, Massachusetts Gen Hosp, Sch Med, CS Kubik Lab Neuropathol,Pathol Serv, Boston, MA USA
[4] Boston Univ, Sch Med, Dept Neurol, Boston, MA 02118 USA
[5] Boston Univ, Sch Med, Dept Pathol, Boston, MA 02118 USA
[6] US Dept Vet Affairs, VA Boston Healthcare Syst, Boston, MA USA
[7] Bedford Vet Adm Med Ctr, Bedford, MA USA
[8] MIT, Comp Sci & AI Lab, Cambridge, MA 02139 USA
[9] Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark
[10] Aalto Univ, Dept Informat & Comp Sci, Helsinki, Finland
[11] Aalto Univ, Dept Biomed Engn & Computat Sci, Helsinki, Finland
基金
美国国家卫生研究院; 加拿大健康研究院;
关键词
MILD COGNITIVE IMPAIRMENT; MEDIAL TEMPORAL-LOBE; ENTORHINAL CORTEX ATROPHY; EARLY ALZHEIMERS-DISEASE; DENTATE GYRUS FUNCTION; T2-WEIGHTED MRI; EPISODIC MEMORY; HUMAN-BRAIN; MAXIMUM-LIKELIHOOD; PATTERN SEPARATION;
D O I
10.1016/j.neuroimage.2015.04.042
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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). (C) 2015 The Authors. Published by Elsevier Inc.
引用
收藏
页码:117 / 137
页数:21
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