A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- NeuroImage
- Topic
- Functional Brain Connectivity Studies
- Field
- Neuroscience
- Canadian institutions
- —
- Funders
- NIH Blueprint for Neuroscience ResearchNational Institute of Mental HealthNational Center for Complementary and Integrative HealthNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchUniversity of California, San DiegoGenentechNational Institutes of HealthDiputación Foral de GipuzkoaNational Institute of Neurological Disorders and StrokeIXICOServierEisaiNorthern California Institute for Research and EducationPfizerBiogenBioClinicaTekesSynarcUniversity of Southern CaliforniaMedpaceNovartis Pharmaceuticals CorporationFramingham Heart StudyU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbAlzheimer's AssociationFoundation for the National Institutes of HealthNational Center for Research ResourcesF. Hoffmann-La RocheEllison Medical FoundationAlzheimer's Disease Neuroimaging InitiativeNational Center for Complementary and Alternative MedicineMeso Scale Diagnostics
- Keywords
- SegmentationComputer scienceAtlas (anatomy)Hippocampal formationArtificial intelligenceNeuroimagingPattern recognition (psychology)Magnetic resonance imagingComputer visionNeuroscienceMedicineAnatomyRadiologyBiology
- Has abstract in OpenAlex
- yes