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.

我们提出了一种半参数生成模型，用于预测基准图像后随后扫描中患者的解剖结构。该模型通过个体基因和临床指标的非参数模型，结合全群回归捕捉解剖学变化。我们演示了在ADNI队列中预测随访解剖扫描，并且说明了比较患者扫描和预测的个体化健康解剖轨迹的新分析方法。

基于遗传和临床数据的解剖预测建模