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Related Experiment Video

Updated: Jun 3, 2026

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection
08:23

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection

Published on: May 9, 2022

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

Shadi Dorosti1, Thomas Landry1, Sidney Croul2

  • 1School of Biomedical Engineering, Dalhousie University, Halifax, NS, Canada.

Ultrasound in Medicine & Biology
|June 1, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an AI and robotics framework for automated glioblastoma (GBM) tumor delineation and histotripsy ablation, improving surgical precision and reducing damage to healthy tissue.

Area of Science:

  • Neurosurgery
  • Medical Robotics
  • Artificial Intelligence

Background:

  • Glioblastoma multiforme (GBM) surgery faces challenges with incomplete margin delineation, leading to residual disease or healthy tissue damage.
  • Automated tumor delineation and guided ablation are crucial for improving surgical outcomes in GBM treatment.

Purpose of the Study:

  • To develop and evaluate an AI- and robotics-enabled closed-loop framework for automated GBM tumor delineation and histotripsy ablation.
  • To enhance surgical precision and minimize damage to healthy tissue during GBM resection.

Main Methods:

  • Deep learning models were trained on intra-operative ultrasound data for real-time tumor segmentation.
  • The segmentation output was integrated with robotic control to guide histotripsy targeting.
Keywords:
Deep learningGL261GlioblastomaHistotripsyImage-guided ablationPreclinical modelReal-time segmentationRoboticsTumor segmentationUltrasound

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Last Updated: Jun 3, 2026

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection
08:23

Implementation of Minimally Invasive Brain Tumor Resection in Rodents for High Viability Tissue Collection

Published on: May 9, 2022

  • The framework was validated in preclinical mouse GBM models (ex vivo and in vivo).

    • Main Results:

    • AI models demonstrated strong real-time tumor segmentation performance.
    • Histotripsy targeting demonstrated good alignment with the intended treatment zones in both ex vivo and in vivo experiments.
    • Minimal undershooting was observed during histotripsy ablation, indicating precise targeting.

    Conclusions:

    • The study supports the feasibility of combining AI-based ultrasound segmentation with robotic guidance for histotripsy targeting in preclinical GBM models.
    • This integrated approach represents a significant step toward increased automation in GBM treatment.
    • The framework shows promise for improving the safety and efficacy of GBM surgery.