Journal article
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
University College London1
University of Castilla-La Mancha2
BCBL – Basque Center on Cognition, Brain and Language3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark5
Massachusetts General Hospital/Harvard Medical School6
Massachusetts Institute of Technology7
The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI.
In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects.
The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei.
When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.
Language: | English |
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Publisher: | Academic Press |
Year: | 2018 |
Pages: | 314-326 |
ISSN: | 10959572 and 10538119 |
Types: | Journal article |
DOI: | 10.1016/j.neuroimage.2018.08.012 |
ORCIDs: | Van Leemput, Koen |