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Neuroimage Signature from Salient Keypoints is Highly Specific to Individuals and Shared by Close Relatives

Institution:
1École de Technologie Supérieure, Montreal, Canada. Electronic address: laurent.chauvin0@gmail.com.
2Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, USA.
3Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
4 Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: pshyn@bwh.harvard.edu.
Publisher:
Elsevier Science
Publication Date:
Jan-2020
Journal:
Neuroimage
Volume Number:
204
Pages:
116208
Citation:
Neuroimage. 2020 Jan 1;204:116208.
PubMed ID:
31546048
PMCID:
PMC6931906
Keywords:
Individual variability, MRI, Neuroimage analysis, Salient image keypoints
Appears in Collections:
NAC, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS
Generated Citation:
Chauvin L., Kumar K., Wachinger C., Vangel M., de Guise J., Desrosiers C., Wells III W.M., Toews M. Neuroimage Signature from Salient Keypoints is Highly Specific to Individuals and Shared by Close Relatives. Neuroimage. 2020 Jan 1;204:116208. PMID: 31546048. PMCID: PMC6931906.
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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.