Estimating patient specific templates for pre-operative and follow-up brain tumor registration

Deformable registration between pre-operative and followup scans of glioma patients is important since it allows us to map postoperative longitudinal progression of the tumor onto baseline scans, thus, to develop predictive models of tumor infiltration and recurrence. This task is very challenging due to large deformations, missing correspondences, and inconsistent intensity profiles between the scans. Here, we propose a new method that combines registration with estimation of patient specific templates. These templates, built from pre-operative and follow-up scans along with a set of healthy brain scans, approximate the patient’s brain anatomy before tumor development. Such estimation provides additional cues for missing correspondences as well as inconsistent intensity profiles, and therefore guides better registration on pathological regions. Together with our symmetric registration framework initialized by joint segmentation-registration using a tumor growth model, we are also able to estimate large deformations between the scans effectively. We apply our method to the scans of 24 glioma patients, achieving the best performance among compared registration methods.