Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives.
In: NeuroImage, Jg. 204 (2020), S. 116208
Online
academicJournal
Zugriff:
Neuroimaging studies typically adopt a common feature space for all data, which may obscure aspects of neuroanatomy only observable in subsets of a population, e.g. cortical folding patterns unique to individuals or shared by close relatives. Here, we propose to model individual variability using a distinctive keypoint signature: a set of unique, localized patterns, detected automatically in each image by a generic saliency operator. The similarity of an image pair is then quantified by the proportion of keypoints they share using a novel Jaccard-like measure of set overlap. Experiments demonstrate the keypoint method to be highly efficient and accurate, using a set of 7536 T1-weighted MRIs pooled from four public neuroimaging repositories, including twins, non-twin siblings, and 3334 unique subjects. All same-subject image pairs are identified by a similarity threshold despite confounds including aging and neurodegenerative disease progression. Outliers reveal previously unknown data labeling inconsistencies, demonstrating the usefulness of the keypoint signature as a computational tool for curating large neuroimage datasets.
(Copyright © 2019 Elsevier Inc. All rights reserved.)
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Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives.
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Autor/in / Beteiligte Person: | Chauvin, L ; Kumar, K ; Wachinger, C ; Vangel, M ; de Guise J ; Desrosiers, C ; Wells, W ; Toews, M |
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Zeitschrift: | NeuroImage, Jg. 204 (2020), S. 116208 |
Veröffentlichung: | Orlando, FL : Academic Press, c1992-, 2020 |
Medientyp: | academicJournal |
ISSN: | 1095-9572 (electronic) |
DOI: | 10.1016/j.neuroimage.2019.116208 |
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