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}.

翻译：术中超声（iUS）成像有望改善脑外科手术的疗效，但其图像解读即便对经验丰富的神经外科医生而言也极具挑战。本研究首次设计了基于患者特异性的无追踪iUS脑肿瘤分割框架。为消除超声成像的歧义性并适应神经外科医生的手术目标，我们利用术前MR数据中模拟虚拟iUS扫描采集生成的合成超声数据，训练了一个患者特异性实时网络。在真实超声数据上进行的大量实验表明，所提方法能够适应外科医生对手术目标的定义，其效果优于非患者特异性模型、神经外科专家及高端追踪系统，充分验证了该方法的有效性。代码见：\url{https://github.com/ReubenDo/MHVAE-Seg}。