Predictive Modeling of Anatomy with Genetic and Clinical Data
This addresses the problem of personalized anatomical prediction for medical imaging analysis, though it appears incremental as it builds on existing generative and regression methods.
The paper tackles predicting a patient's future anatomy from a single baseline scan using genetic and clinical data, demonstrating prediction of follow-up scans in the ADNI cohort and enabling comparison to predicted healthy trajectories.
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. The code is available at https://github.com/adalca/voxelorb.