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Predictive Modeling of Anatomy with Genetic and Clinical Data

  • Adrian V. Dalca
  • Ramesh Sridharan
  • Mert R. Sabuncu
  • Polina Golland
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image. Such predictive modeling promises to facilitate novel analyses in both voxel-level studies and longitudinal biomarker evaluation. We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject’s health based on individual genetic and clinical indicators. In contrast to classical correlation and longitudinal analysis, we focus on predicting new observations from a single subject observation. We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient’s scans to the predicted subject-specific healthy anatomical trajectory.

Keywords

Population Trend Baseline Image Kernel Machine Good Linear Unbiased Predictor Segmentation Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Adrian V. Dalca
    • 1
  • Ramesh Sridharan
    • 1
  • Mert R. Sabuncu
    • 1
    • 2
  • Polina Golland
    • 1
  1. 1.Computer Science and Artificial Intelligence Lab, EECSMITCambridgeUSA
  2. 2.Martinos Center for Biomedical ImagingHarvard Medical SchoolBostonUSA

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