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Analyzing Brain Morphology on the Bag-of-Features Manifold

Institution:
1Ecole de Technologie Superieure, Montreal, Canada. laurent.chauvin0@gmail.com
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publisher:
Springer
Publication Date:
Jun-2019
Volume Number:
LNCS 11492
Pages:
45-56
Citation:
Inf Process Med Imaging. 2019 Jun;11492:45-56.
Appears in Collections:
NAC, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Chauvin L., Kumar K., De Guise J., Wells III. W.M., Toews M. Analyzing Brain Morphology on the Bag-of-Features Manifold. Inf Process Med Imaging. 2019 Jun;11492:45-56.
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We propose a novel distance measure between variable-sized sets of image features, i.e. the bag-of-features image representation, for quantifying brain morphology similarity based on local neuroanatomical structures. Our measure generalizes the Jaccard distance metric to account for probabilistic or soft set equivalence (SSE), via a novel adaptive kernel density framework accounting for probabilistic uncertainty in both feature appearance and geometry. The method is based on highly efficient keypoint feature indexing and is suitable for identifying pairwise relationships in arbitrarily large data sets. Experiments use the Human Connectome Project (HCP) dataset consisting of 1010 subjects, including pairs of siblings and twins, where neuroanatomy is modeled as a set of scale-invariant keypoints extracted from T1-weighted MRI data. The Jaccard distance based on (SSE) is shown to outperform standard hard set equivalence (HSE) in predicting the immediate family graph structure and genetic links such as racial origin and sex from MRI data, providing a useful tool for data-driven, high-throughput genome wide heritability analysis.