Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset
| Autor: | Boris C. Bernhardt, Tommaso Mansi, Kevin E. Liang, Seok-Jun Hong, Jessie Kulaga-Yoskovitz, Jonathan Smallwood, Andre van der Kouwe, Neda Bernasconi, Andrea Bernasconi |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Statistics and Probability
Data Descriptor Computer science Hippocampus Brain imaging Library and Information Sciences Hippocampal formation computer.software_genre 030218 nuclear medicine & medical imaging Education 03 medical and health sciences 0302 clinical medicine Magnetic resonance imaging Gyrus Neuroimaging Image Interpretation Computer-Assisted medicine Humans Segmentation Protocol (object-oriented programming) Brain Mapping medicine.diagnostic_test business.industry Brain Pattern recognition Lobe Computer Science Applications medicine.anatomical_structure nervous system Neurology Dentate Gyrus Artificial intelligence Data mining Statistics Probability and Uncertainty business computer 030217 neurology & neurosurgery Algorithms Information Systems Neuroscience |
| Zdroj: | Scientific Data |
| ISSN: | 2052-4463 |
| Popis: | The hippocampus is composed of distinct anatomical subregions that participate in multiple cognitive processes and are differentially affected in prevalent neurological and psychiatric conditions. Advances in high-field MRI allow for the non-invasive identification of hippocampal substructure. These approaches, however, demand time-consuming manual segmentation that relies heavily on anatomical expertise. Here, we share manual labels and associated high-resolution MRI data (MNI-HISUB25; submillimetric T1- and T2-weighted images, detailed sequence information, and stereotaxic probabilistic anatomical maps) based on 25 healthy subjects. Data were acquired on a widely available 3 Tesla MRI system using a 32 phased-array head coil. The protocol divided the hippocampal formation into three subregions: subicular complex, merged Cornu Ammonis 1, 2 and 3 (CA1-3) subfields, and CA4-dentate gyrus (CA4-DG). Segmentation was guided by consistent intensity and morphology characteristics of the densely myelinated molecular layer together with few geometry-based boundaries flexible to overall mesiotemporal anatomy, and achieved excellent intra-/inter-rater reliability (Dice index ≥90/87%). The dataset can inform neuroimaging assessments of the mesiotemporal lobe and help to develop segmentation algorithms relevant for basic and clinical neurosciences. |
| Databáze: | OpenAIRE |
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