  Intraoperative ultrasound (iUS) imaging has the potential to improve surgical
outcomes in brain surgery. However, its interpretation is challenging, even for
expert neurosurgeons. In this work, we designed the first patient-specific
framework that performs brain tumor segmentation in trackerless iUS. To
disambiguate ultrasound imaging and adapt to the neurosurgeon's surgical
objective, a patient-specific real-time network is trained using synthetic
ultrasound data generated by simulating virtual iUS sweep acquisitions in
pre-operative MR data. Extensive experiments performed in real ultrasound data
demonstrate the effectiveness of the proposed approach, allowing for adapting
to the surgeon's definition of surgical targets and outperforming
non-patient-specific models, neurosurgeon experts, and high-end tracking
systems. Our code is available at: \url{https://github.com/ReubenDo/MHVAE-Seg}.
