Streaming DICOM RealTime Video and Metadata Flows Outside The Operating Room With the current advancement in the medical world, surgeons are faced with the challenge of handling many sources of medical information in more and more complex and technological Operating Rooms (ORs). Obviously, in the next generation ones, there will be an increasing number of video flows during the surgery (e.g. endoscopes, cameras, ultrasounds, etc.), which can be also displayed all over the OR in order to facilitate the task for the surgeon and to avoid any adverse events or problems related to inadequate communication in the OR. Additionally, other information needs to be shared, prepostduring an operation, such as the history of the digital images related to the patient in the PACS and the metadata coming from medical sensors. Moreover, these medical videos captured from the OR can be either displayed on a large screen in the OR in order to provide the surgeon with more visibility, in this case via DICOMRTV, or streamed outside the OR via a P2P solution. The latter one can serve various purposes such as for teaching medical student in realtime or for remoteexpertise with a remote senior surgeons. Hence, this paper addresses the challenges of streaming DICOMRTV video and metadata flows live from the operating room, typically during an ongoing surgery, in realtime to the outside world. A Proof of Concept is also presented in order to demonstrate the feasibility of our solution.

A Deep Architecture for Surgical Workflow Recognition with Edge Information Realtime surgery workflow automatic detection as computerassisted surgery systems has become an emerging trend due to improving patient safety during surgery. Currently, the convolutional neural networks can show the best performance for contentbased video analysis of surgical workflow. In this paper, a novel solution of surgery workflow detection during the procedure was presented, the edge information of original phases from video frames was extracted and then employed to train together with original phases by using a ResNet. Finally, the methods were evaluated on cataract101 dataset, a publicly available dataset for surgical phase analysis, on which a maximum accuracy of 90.1% was reached. Additionally, the accuracy of 3% improvement was achieved when compared with the method of no processing the data by edge detection. It is shown that using the edge information of original images could improve the performance of surgical phase recognition, because it can be complementary information for original images to recognize the surgical workflow. This paper shows valuable potential to develop modern medical diagnosis and treatment in automating workflow recognition, and the edge processing of original phases for recognition images can also produce new features to assist the network to recognize the original images. Furthermore, the technology studied in this paper can also be used in other video analysis tasks, or classification of image tasks.