  A reliable Ultrasound (US)-to-US registration method to compensate for brain
shift would substantially improve Image-Guided Neurological Surgery. Developing
such a registration method is very challenging, due to factors such as missing
correspondence in images, the complexity of brain pathology and the demand for
fast computation. We propose a novel feature-driven active framework. Here,
landmarks and their displacement are first estimated from a pair of US images
using corresponding local image features. Subsequently, a Gaussian Process (GP)
model is used to interpolate a dense deformation field from the sparse
landmarks. Kernels of the GP are estimated by using variograms and a discrete
grid search method. If necessary, the user can actively add new landmarks based
on the image context and visualization of the uncertainty measure provided by
the GP to further improve the result. We retrospectively demonstrate our
registration framework as a robust and accurate brain shift compensation
solution on clinical data acquired during neurosurgery.
