bibtext: @InProceedings{ Dor_PatientSpecific_MICCAI2024,
                   author = { Dorent, Reuben and Torio, Erickson and Haouchine, Nazim and Galvin, Colin and Frisken, Sarah and Golby, Alexandra and Kapur, Tina and Wells III, William M. },
                   title = { { Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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}.
title: Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound
authors: Dorent, Reuben Torio, Erickson Haouchine, Nazim Galvin, Colin Frisken, Sarah Golby, Alexandra Kapur, Tina Wells III, William M.