Towards Real-time Remote Processing of Laparoscopic Video

Laparoscopic surgery is a minimally invasive technique where surgeons insert a small video camera into the patient\u27s body to visualize internal organs and use small tools to perform these procedures. However, 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 and can reduce the limitations of laparoscopic surgery. One particular laparoscopic system is the daVinci-si robotic surgical vision system. The video streams generate approximately 360 megabytes of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Real-time processing this large stream of data on a bedside PC, single or dual node setup, may be challenging and a high-performance computing (HPC) environment is not typically available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second rate (fps), it is required that each 11.9 MB (1080p) video frame be processed by a server and returned within the time this frame is displayed or 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. We have implemented and compared performance of compression, segmentation and registration algorithms on Clemson\u27s Palmetto supercomputer using dual Nvidia graphics processing units (GPUs) per node and compute unified device architecture (CUDA) programming model. We developed three separate applications that run simultaneously: video acquisition, image processing, and video display. The image processing application allows several algorithms to run simultaneously on different cluster nodes and transfer images through message passing interface (MPI). Our segmentation and registration algorithms resulted in an acceleration factor of around 2 and 8 times respectively. To achieve a higher frame rate, we also resized images and reduced the overall processing time. As a result, using high-speed network to access computing clusters with GPUs to implement these algorithms in parallel will improve surgical procedures by providing real-time medical image processing and laparoscopic data

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

Oral Paper SP63. Learner Centred Communication Masterclasses

Background HYMS 3rd and 4th Year MB ChB students frequently encountered communication challenges on clinical placements, despite extensive communication skills teaching in the first two (university based) years of the course. PresentationCompulsory Communication Masterclasses were introduced for 3rd and 4th year students to provide an opportunity for them to address Communication and Professionalism challenges they have encountered on clinical placement. The student-centred Masterclasses are led by Primary /Secondary Care clinicians working with experienced Simulated Patients. They provide an opportunity for students to role play Communication/Professionalism challenges and receive feedback from their peers, Simulated Patient and tutor to help identify strategies for dealing with similar challenges in their future career. Evaluation Students are required to complete an online evaluation which includes descriptive and Likert scale feedback. Students give consistently positive feedback on these sessions, and highlight appreciating the opportunity to reflect and learn from clinician tutors about real-life communication/ professionalism challenges. This student evaluation informs Staff Development Masterclasses for tutors, tutored by faculty and run similarly to the Student Communication Masterclasses. These provide an opportunity to address challenges that tutors have encountered when tutoring Masterclasses and ensure that tutors deliver a consistently high quality student-learning experience

Oral Paper S26 - What are students frightened of?

Background Despite extensive consistent integrated early clinical experience at HYMS, students have often been noted to struggle in making the transition from the largely University-based Phase I (2 years) to immersion in the clinically-based Phase II. Tutors report student difficulties in adopting an appropriate attitude to learning in this environment; some are noted to respond to this by minimising the time spent on the wards with obvious consequences for their experience and education. Presentation A new “Core Clinical Skills and Professional Expectations” course, lasting 2 weeks was introduced in August 2014 for students making this transition. This block aimed to address many areas which students have been noted to struggle with, including professionalism and development of clinical diagnostic reasoning and skills for independent learning. Evaluation Students were asked to identify their own fears and anxieties about moving into the clinical environment. All students completed a brief survey at both the beginning and the end of this two week period which included identification of their own sources of anxiety in approaching immersion in the clinical environment. Results of this survey are presented and discussed with implications for clinical teaching