Brief

Researchers have introduced PRISM, a new framework that combines implicit neural representations with statistical shape analysis to model anatomical shapes and their uncertainties. This approach allows for continuous estimation of population mean and covariate-dependent uncertainty at any location, offering a unified method for tasks like shape evolution modeling, personalized prediction, and anomaly detection. A key theoretical contribution is a closed-form Fisher Information metric that facilitates efficient local temporal uncertainty quantification through automatic differentiation. AI