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)


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.


Population Trend Baseline Image Kernel Machine Good Linear Unbiased Predictor Segmentation Label 
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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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