On-demand lung CT analysis with the M5L-CAD via the WIDEN front-end web interface and an OpenNebula-based cloud back-end

• Published in: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) pp. 978 - 984
• Main Authors: Berzano, Dario, Bagnasco, Stefano, Brunetti, Riccardo, Camarlinghi, Niccolo, Cerello, Piergiorgio, Chauvie, Stephane, De Nunzio, Giorgio, Fiorina, Elisa, Fantacci, Maria E., Lopez Torres, Ernesto, Lusso, Stefano, Peroni, Cristiana, Stancu, Alexandru
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• ISBN: 9781467320283; 1467320285
• ISSN: 1082-3654; 2577-0829
• DOI: 10.1109/NSSMIC.2012.6551253

The development of algorithms for the analysis of medical images has been progressively growing over the past two decades. The most common approach is the deployment of standalone workstations, equipped with provider-dependent Graphic User Interfaces (GUI) from which the algorithm execution is triggered interactively. There are, however, several drawbacks: among them, the GUI development cost, the GUI learning curve for the users, the high fixed cost of the software licenses, the difficulty in upgrading the software release. For a few years, the hypothesis of using Grid Services has been explored by several research groups. It turned out that there were other drawbacks: the high costs and security risks of integrating computing resources of medical centers into a Grid Computing Infrastructure. The emerging of Cloud computing, accessible via secure Web protocols, solves most - if not all - the problems. In the specific case of lung Computer Assisted Detection, a further important reason favors the SaaS (Software as a Service) approach: it was demonstrated by several works that combining CAD algorithms improves the overall performance. The system we present is composed by three main building blocks: WIDEN (Web-based Image and Diagnosis Exchange Network) handles the workflow, the image upload and the CAD result notification; the OpenNebula-based cloud IaaS (Infrastructure as a Service) batch farm allocates virtual computing and storage resources; the M5L CAD provides the nodule detection functionality. Our proposed implementation securely handles sensitive patient data, since images are transferred with the HTTPS protocol and the underlying virtual batch farm is isolated. Moreover it is efficient since it dynamically backend.