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

Citation:

Laurent Chauvin, Kuldeep Kumar, Christian Wachinger, Marc Vangel, Jacques de Guise, Christian Desrosiers, William Wells, and Matthew Toews. 2020. “Neuroimage Signature from Salient Keypoints is Highly Specific to Individuals and Shared by Close Relatives.” Neuroimage, 204, Pp. 116208.

Abstract:

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.
Last updated on 01/06/2020