Background: Open surgery relies heavily on the surgeon's visual acuity and spatial awareness to track instruments within a dynamic and often cluttered surgical field.
Methods: This system utilizes a head-mounted depth camera to monitor surgical scenes, providing both image data and depth information. The video captured from this camera is scaled down, compressed using MPEG, and transmitted to a high-performance workstation via the RTSP (Real-Time Streaming Protocol), a reliable protocol designed for real-time media transmission. To segment surgical instruments, we utilize the enhanced U-Net with GridMask (EUGNet) for its proven effectiveness in surgical tool segmentation.
Results: For rigorous validation, the system's performance reliability and accuracy are evaluated using prerecorded RGB-D surgical videos. This work demonstrates the potential of this system to improve situational awareness, surgical efficiency, and generate data-driven insights within the operating room. In a simulated surgical environment, the system achieves a high accuracy of 85.5% in identifying and segmenting surgical instruments. Furthermore, the wireless video transmission proves reliable with a latency of 200 ms, suitable for real-time processing.
Conclusions: These findings represent a promising step towards the development of assistive technologies with the potential to significantly enhance surgical practice.
Keywords: U-Net; computer vision; convolutional neural network; data augmentation; image processing; intraoperative surgery; monitoring surgical scene; surgical tools segmentation.