Deep evidential uncertainty estimation in joint synthesis and registration of MRI and cone-beam CT images

Purpose. The purpose of this work is to produce uncertainty estimation for the registration task between MR and CBCT based on the joint synthesis and registration network. This novel framework aims to equip surgeons with robust tools for assessing the fidelity of registration, enabling informed decisions regarding the volume alignment, particularly in regions where uncertainty is prevalent. Methods. The prediction of deformation fields is framed as a regression problem, and therefore allows Evidential Deep Learning (EDL) for uncertainty estimation. By utilizing a loss function grounded in likelihood, the network is compelled to simultaneously learn the parameters of EDL and generate precise predictions. This adaptive approach is tailored specifically for the registration task, rendering the estimation method notably flexible for unsupervised registration. Results. The visualization of the uncertainty map demonstrates areas where the registered ventricle deviates from the ground truth alignment. Additionally, the incorporation of uncertainty assessment enhances registration performance, as evidenced by quantitative evaluations across seven distinct brain regions. Comparative analysis between networks trained with and without uncertainty underscores the performance of the uncertainty-aware model in achieving more accurate registrations. Conclusions. The preliminary results show that incorporating uncertainty estimation into registration model leads to improved registration performance. Furthermore, the estimated uncertainty map is also useful for surgeons to evaluate the reliability of registered images.