Predictive Modeling of Anatomy with Genetic and Clinical Data
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
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Computer Science and Artificial Intelligence Lab, EECS, MIT Martinos Center for Biomedical Imaging, Harvard Medical School
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.
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Introduction
We present a method for predicting anatomy based on external information, including genetic and clinical indicators. Specifically, given only a single baseline scan of a new subject in a longitudinal study, our model predicts anatomical changes and generates a subsequent image by leveraging subject-specific genetic and clinical information. Such voxel-wise prediction opens up several new areas of analysis, enabling novel investigations both at the voxel level and at the level of derivative biomarker measures. For example, voxel level differences between the true progression of a patient with dementia and their predicted healthy anatomy highlight spatial patterns of disease. We validate our method by comparing measurements of volumes of anatomical structures based on predicted images to those extracted from the acquired scans. Our model describes the change from a single (or baseline) medical scan in terms of population trends and subject-specific external information. We model how anatomical appearance changes with age on average in a population, as well as deviations from the population average using a person’s health profile. We characterize such profiles non-parametrically based on the genotype, clinical information, and the baseline image. Subject-specific change is constructed Æ
A listing of ADNI investigators is available at http://tinyurl.com/ADNI-main. Data used via AD DREAM Challenge: https://www.synapse.org/ #!Synapse:syn2290704
c Springer International Publishing Switzerland 2015 N. Navab et al. (Eds.): MICCAI 2015, Part III, LNCS 9351, pp. 519–526, 2015. DOI: 10.1007/978-3-319-24574-4_62
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from the similarity of health profiles in the cohort, using a Gaussian process parametrized by a population health covariance. Given the predicted change, we synthesize new images through an appearance model. Statistical population analysis is one of the central topics in medical image computing. The classical correlation-based analysis has yielded important characterization of relationships within imaging data and with independent clinical variables [2,11,12,14
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