Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound

TL;DR

This paper introduces a novel patient-specific, real-time brain tumor segmentation framework for trackerless intraoperative ultrasound, improving surgical targeting by adapting to individual patient data and surgeon objectives.

Contribution

It is the first to develop a patient-specific, real-time segmentation method for trackerless brain ultrasound using synthetic data for training.

Findings

Outperforms non-patient-specific models

Outperforms neurosurgeon experts

Outperforms high-end tracking systems

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}.