18 March 2016 HPC enabled real-time remote processing of laparoscopic surgery
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Abstract
Laparoscopic surgery is a minimally invasive surgical technique. The benefit of small incisions has a disadvantage of limited visualization of subsurface tissues. Image-guided surgery (IGS) uses pre-operative and intra-operative images to map subsurface structures. One particular laparoscopic system is the daVinci-si robotic surgical system. The video streams generate approximately 360 megabytes of data per second. Real-time processing this large stream of data on a bedside PC, single or dual node setup, 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 rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second.

We have implement and compared performance of compression, segmentation and registration algorithms on Clemson's Palmetto supercomputer using dual NVIDIA K40 GPUs per node. Our computing framework will also enable reliability using replication of computation. We will securely transfer the files to remote HPC clusters utilizing an OpenFlow-based network service, Steroid OpenFlow Service (SOS) that can increase performance of large data transfers over long-distance and high bandwidth networks. As a result, utilizing high-speed OpenFlow- based network to access computing clusters with GPUs will improve surgical procedures by providing real-time medical image processing and laparoscopic data.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zahra Ronaghi, Karan Sapra, Ryan Izard, Edward Duffy, Melissa C. Smith, Kuang-Ching Wang, David M. Kwartowitz, "HPC enabled real-time remote processing of laparoscopic surgery", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97861U (18 March 2016); doi: 10.1117/12.2217153; https://doi.org/10.1117/12.2217153
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