Predictive Modeling of Anatomy with Genetic and Clinical Data
Author(s)Dalca, Adrian Vasile; Sridharan, Ramesh; Sabuncu, Mert R; Golland, Polina
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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
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
Dalca, Adrian V., et al. “Predictive Modeling of Anatomy with Genetic and Clinical Data.” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by Nassir Navab et al., vol. 9351, Springer International Publishing, 2015, pp. 519–26.
Author's final manuscript