Streaming DICOM Real-Time Video and Metadata Flows Outside The Operating Room

International audienceWith 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, pre/post/during 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 DICOM-RTV, or streamed outside the OR via a P2P solution. The latter one can serve various purposes such as for teaching medical student in real-time or for remote-expertise with a remote senior surgeons. Hence, this paper addresses the challenges of streaming DICOM-RTV video and metadata flows live from the operating room, typically during an ongoing surgery, in real-time to the outside world. A Proof of Concept is also presented in order to demonstrate the feasibility of our solution

Peer-to-Peer File Sharing WebApp: Enhancing Data Security and Privacy through Peer-to-Peer File Transfer in a Web Application

Peer-to-peer (P2P) networking has emerged as a promising technology that enables distributed systems to operate in a decentralized manner. P2P networks are based on a model where each node in the network can act as both a client and a server, thereby enabling data and resource sharing without relying on centralized servers. The P2P model has gained considerable attention in recent years due to its potential to provide a scalable, fault-tolerant, and resilient architecture for various applications such as file sharing, content distribution, and social networks.In recent years, researchers have also proposed hybrid architectures that combine the benefits of both structured and unstructured P2P networks. For example, the Distributed Hash Table (DHT) is a popular hybrid architecture that provides efficient lookup and search algorithms while maintaining the flexibility and adaptability of the unstructured network.To demonstrate the feasibility of P2P systems, several prototypes have been developed, such as the BitTorrent file-sharing protocol and the Skype voice-over-IP (VoIP) service. These prototypes have demonstrated the potential of P2P systems for large-scale applications and have paved the way for the development of new P2P-based systems

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Liquid stream processing on the web: a JavaScript framework

The Web is rapidly becoming a mature platform to host distributed applications. Pervasive computing application running on the Web are now common in the era of the Web of Things, which has made it increasingly simple to integrate sensors and microcontrollers in our everyday life. Such devices are of great in- terest to Makers with basic Web development skills. With them, Makers are able to build small smart stream processing applications with sensors and actuators without spending a fortune and without knowing much about the technologies they use. Thanks to ongoing Web technology trends enabling real-time peer-to- peer communication between Web-enabled devices, Web browsers and server- side JavaScript runtimes, developers are able to implement pervasive Web ap- plications using a single programming language. These can take advantage of direct and continuous communication channels going beyond what was possible in the early stages of the Web to push data in real-time. Despite these recent advances, building stream processing applications on the Web of Things remains a challenging task. On the one hand, Web-enabled devices of different nature still have to communicate with different protocols. On the other hand, dealing with a dynamic, heterogeneous, and volatile environment like the Web requires developers to face issues like disconnections, unpredictable workload fluctuations, and device overload. To help developers deal with such issues, in this dissertation we present the Web Liquid Streams (WLS) framework, a novel streaming framework for JavaScript. Developers implement streaming operators written in JavaScript and may interactively and dynamically define a streaming topology. The framework takes care of deploying the user-defined operators on the available devices and connecting them using the appropriate data channel, removing the burden of dealing with different deployment environments from the developers. Changes in the semantic of the application and in its execution environment may be ap- plied at runtime without stopping the stream flow. Like a liquid adapts its shape to the one of its container, the Web Liquid Streams framework makes streaming topologies flow across multiple heterogeneous devices, enabling dynamic operator migration without disrupting the data flow. By constantly monitoring the execution of the topology with a hierarchical controller infrastructure, WLS takes care of parallelising the operator execution across multiple devices in case of bottlenecks and of recovering the execution of the streaming topology in case one or more devices disconnect, by restarting lost operators on other available devices

Implementación de videovigilancia mediante streaming en aeropuertos

La finalidad de este proyecto, es por un lado, utilizar el potencial que ofrecen los dispositivos móviles para aportar nuevas funcionalidades a los sistemas de videovigilancia aeroportuaria, y por otro lado, aprovechar los avances en las tecnologías, con el apoyo de plataformas de código abierto, para crear un sistema de videovigilancia que nos aporte seguridad, eficiencia, versatilidad y bajo coste

Real-time sentiment analysis of video calls

In recent years, with ever-increasing internet connection speed and bandwidth, video-focused software has become more and more popular for both work and pleasure. Examples of such applications include Skype, BlueJeans or iOS Face- Time. These applications, and the various interactions facilitated by them contain lots of interesting data that we feel would be very fruitful to gather and analyze. Within the context of this thesis, we focused on evaluating the potential of collecting sentiment analytics from video teleconferencing both on an individual and group level, for the purpose of helping people reflect on their own behavior and regulate their emotions . To achieve this, we developed a composable, scalable microservice-based analytics pipeline for video and speech, and a browser-based web application to demonstrate it. We evaluated already existing solutions for gathering sentiment analytics, and integrated two of them into our analytics pipeline. The whole system was deployed in a virtualized container environment using Docker. Be- sides the pipeline and web application, we also designed and implemented some visualizations for the data that we gathered. In the end we developed a working prototype, although deeper analysis and evaluation of the actual accuracy of its results needs to be performed. Human emotions are rather difficult to quantize. We found that the current APIs and libraries publicly available for performing sentiment analysis are already quite accurate and feature-rich, and we expect them to get even better