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Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient’s body to visualize internal organs and use small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery uses the images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, California). The video streams generate approximately 360 MB of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We have performed image processing algorithms on a high-definition head phantom video (1920 × 1080 pixels) and transferred the video using a message passing interface. The total transfer time is around 53 ms or 19 fps. We will optimize and parallelize these algorithms to reduce the total time to 30 ms.
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