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Landmark-Guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection.

Jiawei Ge1, Hamed Saeidi1, Justin D Opfermann2

  • 1Department of Mechanical Engineering, University of Maryland, College Park, MD, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 29, 2021
PubMed
Summary

This study introduces a new near-infrared fluorescent marking and deformable image registration method to accurately track oral cancer resection margins. This technique improves surgical precision for oral squamous cell carcinoma (OSCC) treatment.

Keywords:
Deformable image registrationImage-guided surgeryMedical robotics

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Area of Science:

  • Oncology
  • Medical Imaging
  • Surgical Robotics

Background:

  • Oral squamous cell carcinoma (OSCC) presents significant morbidity and mortality, with surgical resection being the primary treatment.
  • Accurate identification and tracking of tumor resection margins are critical for successful OSCC surgery, yet challenging due to margin migration and intra-operative deformation.
  • Current methods struggle to precisely delineate margins, leading to suboptimal surgical outcomes like impaired organ function or tumor recurrence.

Purpose of the Study:

  • To develop and evaluate a novel near-infrared (NIR) fluorescent marking and landmark-based deformable image registration (DIR) method for precise prediction of deformed OSCC resection margins.
  • To compare the accuracy of the DIR method against rigid image registration and manual surgeon predictions.
  • To demonstrate the feasibility of integrating this technique into a robotic system for supervised autonomous tumor bed resections.

Main Methods:

  • A novel NIR fluorescent marking system was employed in conjunction with landmark-based deformable image registration (DIR).
  • The accuracy of DIR-predicted resection margins was assessed on porcine cadaver tongues and compared to rigid registration and surgeon estimations.
  • The developed tracking and registration technique was integrated into a robotic surgical system for ex vivo testing.

Main Results:

  • The novel NIR marking and DIR method demonstrated precise prediction of deformed resection margins on porcine cadaver tongues.
  • DIR showed superior accuracy in margin prediction compared to both rigid image registration and surgeon's manual predictions.
  • Integration with a robotic system confirmed the feasibility of supervised autonomous tumor bed resections using the developed technique.

Conclusions:

  • The developed NIR fluorescent marking and DIR method offers a promising solution for accurately tracking and predicting deformed resection margins in OSCC surgery.
  • This approach has the potential to improve surgical outcomes by enabling more precise tumor resections and reducing recurrence rates.
  • The successful integration into a robotic system highlights the future potential for enhanced, autonomous surgical procedures in head and neck oncology.