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Patient-Specific Real-Time Segmentation in Trackerless Brain Ultrasound.

Reuben Dorent1, Erickson Torio1, Nazim Haouchine1

  • 1Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 22, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel patient-specific framework for brain tumor segmentation using intraoperative ultrasound (iUS) imaging. The approach enhances surgical precision by adapting to individual patient data and surgeon objectives, outperforming existing methods.

Keywords:
Cross-Modal SynthesisImage SegmentationIntraoperative UltrasoundNeurosurgery

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Intraoperative ultrasound (iUS) offers potential for improved brain surgery outcomes.
  • Interpreting iUS is challenging for neurosurgeons.
  • Accurate brain tumor segmentation is crucial for surgical planning and execution.

Purpose of the Study:

  • To develop the first patient-specific framework for trackerless intraoperative ultrasound brain tumor segmentation.
  • To adapt iUS interpretation to neurosurgical objectives in real-time.
  • To improve the accuracy and reliability of brain tumor segmentation during surgery.

Main Methods:

  • A patient-specific real-time network was designed for brain tumor segmentation in trackerless iUS.
  • Synthetic ultrasound data was generated by simulating virtual iUS sweep acquisitions from pre-operative MRI.
  • The network was trained using this synthetic data to disambiguate iUS and adapt to surgical goals.

Main Results:

  • The proposed framework demonstrated effectiveness in segmenting brain tumors using real intraoperative ultrasound data.
  • The approach successfully adapted to surgeons' definitions of surgical targets.
  • Patient-specific models outperformed non-patient-specific models, expert neurosurgeons, and high-end tracking systems.

Conclusions:

  • The developed patient-specific framework significantly enhances brain tumor segmentation in trackerless iUS.
  • This technology has the potential to improve surgical navigation and outcomes in neurosurgery.
  • The framework offers a adaptable and high-performing solution for real-time surgical guidance.