HPC Enabled Real-Time Remote Processing of Laparoscopic Surgery
Authors: Karan Sapra (Clemson University), Zahra Ronaghi (Clemson University), Ryan Izard (Clemson University), Edward Duffy (Clemson University), Melissa C. Smith (Clemson University), Kuang-Ching Wang (Clemson University), David M. Kwartowitz (Clemson University)
Abstract: 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 subsurface tissue visualization. Image-guided surgery (IGS) uses images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system is the vision system of the daVinci robotic surgical system. The video streams generate approximately 360 MB of data per second, demonstrating a trend towards increased data sizes in medicine. Processing this huge stream of data on a single or dual node setup is a challenging task, thus we propose High Performance Computing (HPC) enabled framework for laparoscopic surgery. We utilize high-speed networks to access computing clusters to perform various operations on pre- and intra-operative images in a secure, reliable and scalable manner.
Two-page extended abstract: pdf