title: Predictive Modeling of Anatomy with Genetic and Clinical Data.
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.
authors: Computer Science and Artificial Intelligence Lab, EECS, MIT.
authors: Adrian V
authors: AV
authors: Dalca
authors: Computer Science and Artificial Intelligence Lab, EECS, MIT.
authors: Ramesh
authors: R
authors: Sridharan
authors: Computer Science and Artificial Intelligence Lab, EECS, MIT; Martinos Center for Biomedical Imaging, Harvard Medical School.
authors: Mert R
authors: MR
authors: Sabuncu
authors: Computer Science and Artificial Intelligence Lab, EECS, MIT.
authors: Polina
authors: P
authors: Golland