Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators.

ABSTRACT: We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discrete and ordinal variables into the modeling. We build Bayesian conditional models based on observed partial clinical indicators, features or shape based on Gaussian processes capturing the dependency structure. We apply the proposed method on a stroke datase

SUBMITTER: Egger B 

PROVIDER: S-EPMC7267042 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature