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 l...
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2017
|
| Online Access: | http://hdl.handle.net/1721.1/108059 |
| _version_ | 1826194996063109120 |
|---|---|
| author | Iglesias, Juan Eugenio Augustinack, Jean C. Nguyen, Khoa Player, Christopher M. Player, Allison Wright, Michelle Roy, Nicole Frosch, Matthew P. McKee, Ann C. Wald, Lawrence L. Fischl, Bruce Van Leemput, Koen |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Iglesias, Juan Eugenio Augustinack, Jean C. Nguyen, Khoa Player, Christopher M. Player, Allison Wright, Michelle Roy, Nicole Frosch, Matthew P. McKee, Ann C. Wald, Lawrence L. Fischl, Bruce Van Leemput, Koen |
| author_sort | Iglesias, Juan Eugenio |
| collection | MIT |
| description | 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). |
| first_indexed | 2024-09-23T10:05:08Z |
| format | Article |
| id | mit-1721.1/108059 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:05:08Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1080592022-09-30T18:50:27Z A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI Iglesias, Juan Eugenio Augustinack, Jean C. Nguyen, Khoa Player, Christopher M. Player, Allison Wright, Michelle Roy, Nicole Frosch, Matthew P. McKee, Ann C. Wald, Lawrence L. Fischl, Bruce Van Leemput, Koen Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Fischl, Bruce 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). National Center for Research Resources (U.S.) (P41EB015896) National Center for Research Resources (U.S.) (BIRN002 U24 RR021382) National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB013565) National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB006758) National Institute on Aging (AG022381) National Institute on Aging (5R01AG008122-22) National Institute on Aging (K01AG028521) National Institute on Aging (P30AG13846) National Institute on Aging (R01AG1649) National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01) National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01) National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01) National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963) National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534) United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR023401) United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR019307) United States. Dept. of Health and Human Services. Shared Instrumentation Grant Program (1S10RR023043) Ellison Medical Foundation National Institutes of Health (U.S.). Blueprint for Neuroscience Research (5U01-MH093765) United States. National Institutes of Health (P30-AG010129) United States. National Institutes of Health (K01-AG030514) 2017-04-11T20:03:53Z 2017-04-11T20:03:53Z 2015-04 2014-07 Article http://purl.org/eprint/type/JournalArticle 1053-8119 http://hdl.handle.net/1721.1/108059 Iglesias, Juan Eugenio; Augustinack, Jean C.; Nguyen, Khoa; Player, Christopher M.; Player, Allison; Wright, Michelle; Roy, Nicole; et al. “A Computational Atlas of the Hippocampal Formation Using Ex Vivo, Ultra-High Resolution MRI: Application to Adaptive Segmentation of in Vivo MRI.” NeuroImage 115 (July 2015): 117–137. © 2015 The Authors en_US http://dx.doi.org/10.1016/j.neuroimage.2015.04.042 NeuroImage Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Elsevier |
| spellingShingle | Iglesias, Juan Eugenio Augustinack, Jean C. Nguyen, Khoa Player, Christopher M. Player, Allison Wright, Michelle Roy, Nicole Frosch, Matthew P. McKee, Ann C. Wald, Lawrence L. Fischl, Bruce Van Leemput, Koen A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title | A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title_full | A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title_fullStr | A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title_full_unstemmed | A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title_short | A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI |
| title_sort | computational atlas of the hippocampal formation using ex vivo ultra high resolution mri application to adaptive segmentation of in vivo mri |
| url | http://hdl.handle.net/1721.1/108059 |
| work_keys_str_mv | AT iglesiasjuaneugenio acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT augustinackjeanc acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT nguyenkhoa acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT playerchristopherm acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT playerallison acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT wrightmichelle acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT roynicole acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT froschmatthewp acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT mckeeannc acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT waldlawrencel acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT fischlbruce acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT vanleemputkoen acomputationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT iglesiasjuaneugenio computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT augustinackjeanc computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT nguyenkhoa computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT playerchristopherm computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT playerallison computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT wrightmichelle computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT roynicole computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT froschmatthewp computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT mckeeannc computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT waldlawrencel computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT fischlbruce computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri AT vanleemputkoen computationalatlasofthehippocampalformationusingexvivoultrahighresolutionmriapplicationtoadaptivesegmentationofinvivomri |