Optimising Image-guided Needle Biopsy Procedures
Keywords:
Image-guided needle biopsy, computer vision, surgical robotic arm, medical imagingAbstract
A minimally invasive technique called image-guided needle biopsy takes tissue samples for diagnosis or treatment. Needle biopsy operations can be carried out more precisely and accurately with surgical robotic arms than with manual techniques. However, controlling surgical robotic arms can be difficult because the patient's movements and any obstructions in the needle's route must be considered. Surgical robotic arms can be controlled more effectively using computer vision technologies when performing image-guided needle biopsy procedures. Using computer vision, real-time tracking of the needle tip location, detection and avoidance of obstructions in the needle's path, motion compensation for the patient, and instantaneous feedback to the surgeon regarding the procedure's success are all possible. Image cancer recognition and tracking system was developed using computer vision to extract cancer center coordinates from the computer vision pixel images. The significance of the suggested approach lies in its ability to raise the effectiveness and precision of surgical robotic arm control, allowing surgeons to execute a more extensive variety of procedures with more ease and precision. The study also includes the investigation of multi-modal imaging modalities, including CT, MRI, and ultrasound studies, leveraging computer vision to localize tissue and guide the needles precisely. The goal of creating a system that coordinates localization and segment brain tumors was achieved. The center coordinates of brain tumors have been extracted from CT scan pictures by effectively applying computer vision techniques. This resulted in increased procedure accuracy and precision, a lower risk of damaging blood vessels and nerves, better surgeon visualization of the procedure, and reduced number of needle insertions required. Overall, the safety and effectiveness of this crucial medical process could be raised by using computer vision to optimize the control of surgical robotic arms during image-guided needle biopsy procedures.
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