Fast Geodesic Regression for Population-Based Image Analysis
Name
nihms934490.pdf
Description
Accepted version
Size
832.46 KB
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Checksum (MD5)
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Author(s)
Hong, Yi
Golland, Polina
Zhang, Miaomiao
Date Issued
September 2017
Journal
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Citation
Hong, Yi et al. "Fast Geodesic Regression for Population-Based Image Analysis." MICCAI 2017: Medical Image Computing and Computer Assisted Intervention, Lecture Notes in Computer Science, 10433, Springer International Publishing, 2017, 317-325. © 2017 Springer International Publishing
Version
Author's final manuscript
Abstract
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challenges for the current regression approaches and prohibits large scale studies. In this paper, we present a fast geodesic regression method that dramatically decreases the computational cost of the inference procedure while maintaining prediction accuracy. We employ an efficient low dimensional representation of diffeomorphic transformations derived from the image data and characterize the regressed trajectory in the space of diffeomorphisms by its initial conditions, i.e., an initial image template and an initial velocity field computed as a weighted average of pairwise diffeomorphic image registration results. This construction is achieved by using a first-order approximation of pairwise distances between images. We demonstrate the efficiency of our model on a set of 3D brain MRI scans from the OASIS dataset and show that it is dramatically faster than the state-of-the-art regression methods while producing equally good regression results on the large subject cohort.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-66182-7_37
