Soft tissue deformation tracking for robotic assisted minimally invasive surgery

Area of Science:

• Robotics
• Medical Imaging
• Biomedical Engineering

Background:

• Accurate tracking of soft tissue deformation is crucial for robotic assisted minimally invasive surgery.
• Existing methods face challenges with occlusions and specular highlights, impacting reliability.

Purpose of the Study:

• To develop and validate a robust framework for real-time soft tissue deformation tracking.
• To improve the precision and consistency of surgical tool guidance in minimally invasive procedures.

Main Methods:

• Combines optical feature tracking from stereo-laparoscope images with a constrained geometrical surface model.
• The model deforms with feature motion, ensuring consistent temporal deformation and surface constraints.
• The framework is designed to be resilient to occlusions and specular highlights.

Main Results:

• Validated accuracy and robustness using a phantom heart model with known ground truth.
• Demonstrated practical value through successful in vivo results.
• The proposed method provides reliable tracking even in challenging visual conditions.

Conclusions:

• The developed framework offers a significant advancement in tracking soft tissue deformation for robotic surgery.
• The method's resilience and accuracy support enhanced surgical navigation and outcomes.
• This approach holds potential for improving safety and efficacy in minimally invasive robotic procedures.