Mussabayeva, AyagozKroshnin, AlexeyKurmukov, AnvarDodonova, YuliaShen, LiCong, ShanWang, LeiGutman, Boris A.2019-08-132019-08-132018-09Mussabayeva, A., Kroshnin, A., Kurmukov, A., Denisova, Y., Shen, L., Cong, S., ... & Gutman, B. A. (2018, September). Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms. In International Workshop on Shape in Medical Imaging (pp. 160-168). Springer, Cham.https://hdl.handle.net/1805/20348We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.enPublisher Policyimage registrationmachine learningsubcortical shapeArticle