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Exploratory Population Analysis with Unbalanced Optimal Transport

  • Samuel GerberEmail author
  • Marc Niethammer
  • Martin Styner
  • Stephen Aylward
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11072)

Abstract

The plethora of data from neuroimaging studies provide a rich opportunity to discover effects and generate hypotheses through exploratory data analysis. Brain pathologies often manifest in changes in shape along with deterioration and alteration of brain matter, i.e., changes in mass. We propose a morphometry approach using unbalanced optimal transport that detects and localizes changes in mass and separates them from changes due to the location of mass. The approach generates images of mass allocation and mass transport cost for each subject in the population. Voxelwise correlations with clinical variables highlight regions of mass allocation or mass transfer related to the variables. We demonstrate the method on the white and gray matter segmentations from the OASIS brain MRI data set. The separation of white and gray matter ensures that optimal transport does not transfer mass between different tissues types and separates gray and white matter related changes. The OASIS data set includes subjects ranging from healthy to mild and moderate dementia, and the results corroborate known pathology changes related to dementia that are not discovered with traditional voxel-based morphometry. The transport-based morphometry increases the explanatory power of regression on clinical variables compared to traditional voxel-based morphometry, indicating that transport cost and mass allocation images capture a larger portion of pathology induced changes.

Notes

Acknowledgments

This work was funded, in part, by NIH grants R01EB021391, R01HD055741, U54HD079124, R42NS086295, R44NS081792, R44CA165621, and R01EB021396 and by NSF grant ECCS-1711776.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Samuel Gerber
    • 1
    Email author
  • Marc Niethammer
    • 2
  • Martin Styner
    • 2
  • Stephen Aylward
    • 1
  1. 1.Kitware Inc.CarborroUSA
  2. 2.University of North CarolinaChapel HillUSA

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