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

## TL;DR

This paper introduces a flexible joint modeling approach for anatomical shapes, image features, and clinical data using copulas, enabling personalized and interpretable analysis in medical imaging.

## Contribution

It presents a novel copula-based framework that separates dependency structures from variable distributions, accommodating diverse data types for patient-specific modeling.

## Key findings

- Effective modeling of shape, image features, and clinical indicators in stroke data
- Flexible inclusion of binary, discrete, and ordinal variables
- Interpretable joint models for data exploration and analysis

## 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 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.