Target Tissue Identification Based on Image Processing for Regulating Automatic Robotic Lung Biopsy Sampler: Onsite...

Area of Science:

• Robotics
• Medical Imaging
• Oncology

Background:

• Cancer diagnosis relies heavily on biopsies, but manual procedures face challenges like tissue movement and targeting accuracy, especially in lung biopsies.
• Robotic assistance offers potential solutions to enhance precision and automation in biopsy procedures.

Purpose of the Study:

• To design and implement an autonomous image processing algorithm within a closed-loop controller for a robotic manipulator to perform emulated lung biopsies.
• To accurately identify cancerous tissue, determine target trajectories, and guide a robotic biopsy needle.

Main Methods:

• Development of a six-degree-of-freedom robotic manipulator integrated with an image processing algorithm for autonomous operation.
• Utilized a realistic lung motion emulator with a photon emitter to simulate radiopharmaceutical identification of cancerous tissue.
• Implemented a finite-time convergent controller to guide the robotic manipulator's end effector to the tumor's centroid within a defined time.

Main Results:

• The image processing algorithm successfully identified the centroid and boundaries of simulated cancerous tissue.
• The robotic system achieved precise tracking of the emulated tumor with a deviation of 0.52 mm.
• The controller demonstrated effective guidance, with a settling time of less than 1 second for trajectory tracking.

Conclusions:

• The autonomous robotic system with image processing and a finite-time convergent controller shows significant promise for accurate and efficient lung biopsies.
• This technology can potentially overcome manual biopsy limitations, improving diagnostic procedures for lung cancer.