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During neurosurgical operations, surgeons can decide to acquireintraoperative data to better proceed with the removal of a tumor. A validoption is given by ultrasound (US) imaging, which can be easily obtained atsubsequent surgical stages, giving therefore multiple updates of the resectioncavity. To improve the efficacy of the intraoperative guidance, neurosurgeonsmay benefit from having a direct correspondence between anatomical structuresidentified at different US acquisitions. In this context, the commonlyavailable neuronavigation systems already provide registration methods, whichhowever are not enough accurate to overcome the anatomical changes happeningduring resection. Therefore, our aim with this work is to improve theregistration of intraoperative US volumes. In the proposed methodology, first adistance mapping of automatically segmented anatomical structures is computedand then the transformed images are utilized in the registration step. Oursolution is tested on a public dataset of 17 cases, where the average landmarkregistration error between volumes acquired at the beginning and at the end ofneurosurgical procedures is reduced from 3.55mm to 1.27mm.

Registration of ultrasound volumes based on Euclidean distance transform