A Neurosurgical Instrument Segmentation Approach to Assess Microsurgical Movements.

The ability to recognize anatomical landmarks, microsurgical instruments, and complex scenes and events in a surgical wound using computer vision presents new opportunities for studying microsurgery effectiveness. In this study, we aimed to develop an artificial intelligence-based solution for detecting, segmenting, and tracking microinstruments using a neurosurgical microscope. We have developed a technique to process videos from microscope camera, which involves creating a segmentation mask for the instrument and subsequently tracking it.

Computer Vision for Assessing Surgical Movements in Neurosurgery.

Objective evaluation of microsurgical technique quality is vital for successful training in neurosurgery. This study aimed to assess the accuracy of automatically detecting a neurosurgeon's proper posture and hand positioning using computer vision. We employed the RTMPose neural network model to identify key anatomical points in the neurosurgeon's projection and calculated various angles formed by connecting these points.