论文标题
通过遗传和临床数据对解剖学的预测建模
Predictive Modeling of Anatomy with Genetic and Clinical Data
论文作者
论文摘要
我们提出了一个半参数生成模型，用于在单个基线图像后在随后的扫描中预测患者的解剖结构。这种预测性建模有望促进体素水平研究和纵向生物标志物评估中的新分析。我们通过基于个体遗传和临床指标的人口回归和非参数模型的结合来捕获解剖学变化。与经典的相关性和纵向分析相反，我们专注于预测单个主题观察的新观察结果。我们证明了对ADNI队列中随访解剖学扫描的预测，并说明了一种新颖的分析方法，该方法将患者的扫描与预测的受试者特异性的健康解剖轨迹进行了比较。该代码可在https://github.com/adalca/voxelorb上找到。
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.
