Bojan Kocev

• We address different aspects of the problem of uncertainty in image-guided therapies. This is of great interest, because such uncertainty could have a substantial impact on public health. Significant uncertainties can occur in image segmentation and estimating deformations of anatomy. To model the uncertainty in the image segmentation aspect, we propose a novel stationary Gaussian process (GP)-based generative segmentation model. This segmentation model allows us to draw many possible image segmentations, which can be used for estimating and visualizing different aspects of the uncertainty in image-guided therapies. To enable drawing of many image segmentation samples efficiently, we propose a fast method for sampling from stationary GPs. To model the uncertainty in the aspect of estimating deformations of anatomy, we propose a novel spatiotemporal GP model for uncertainty-aware soft-tissue motion estimation using GP regression. The spatiotemporal GP formalism enables the estimation of anatomy displacements at any location, and for any time interval from measured motions that are sparse in space and time. The use of GP regression enables the quantification of uncertainty in the soft-tissue motion estimation result, which allows the amount of uncertainty in some aspects, e.g., registered planning medical images, of image-guided therapies or procedures governing the decisions of medical specialists to be conveyed. To convey the amount of uncertainty in the anatomy motion estimates, we propose novel motion uncertainty visualization methods. To showcase the use of the devised methods, we deploy them in the context of radiotherapy and image-guided soft-tissue intervention navigation. We expect that incorporating estimates of spatial and temporal uncertainty into the processing pipelines of image-guided therapy will eventually enable improved treatments, and thus, improved outcomes.