Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
This work addresses the need for flexible and interpretable statistical models in medical image analysis, particularly for patient-specific applications, but it is incremental as it builds on existing copula and Bayesian methods.
The authors tackled the problem of jointly modeling anatomical shapes, image features, and clinical indicators in medical data by proposing a copula-based method that separates dependency structures from marginal distributions, resulting in interpretable joint models for a stroke dataset.
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 dataset to jointly model the shape of the lateral ventricles, the spatial distribution of the white matter hyperintensity associated with periventricular white matter disease, and clinical indicators. The proposed method yields interpretable joint models for data exploration and patient-specific statistical shape models for medical image analysis.