GIFT-Cloud: A data sharing and collaboration platform for medical imaging research. (February 2017)

• Record Type: Journal Article
• Authors: Doel, Tom; Shakir, Dzhoshkun I.; Pratt, Rosalind; Aertsen, Michael; Moggridge, James; Bellon, Erwin; David, Anna L.; Deprest, Jan; Vercauteren, Tom; Ourselin, Sébastien
• Is Part Of: Computer methods and programs in biomedicine. Volume 139(2017)
• Page Start: 181
• Page End: 190
• Publication Date: 2017-02
• Subjects: Data sharing -- Biomedical research -- Cross-disciplinary research -- Anonymisation -- Deidentification -- Fetal surgery; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.11.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A platform for sharing medical imaging data between clinicians and researchers. Extensible system connects three hospitals and two universities. Simple for end users with low impact on hospital IT systems. Automated anonymisation of pixel data and metadata at the clinical site. Maintains subject data groupings while preserving anonymity. Abstract: Objectives: Clinical imaging data are essential for developing research software for computer-aided diagnosis, treatment planning and image-guided surgery, yet existing systems are poorly suited for data sharing between healthcare and academia: research systems rarely provide an integrated approach for data exchange with clinicians; hospital systems are focused towards clinical patient care with limited access for external researchers; and safe haven environments are not well suited to algorithm development. We have established GIFT-Cloud, a data and medical image sharing platform, to meet the needs of GIFT-Surg, an international research collaboration that is developing novel imaging methods for fetal surgery. GIFT-Cloud also has general applicability to other areas of imaging research. Methods: GIFT-Cloud builds upon well-established cross-platform technologies. The Server provides secure anonymised data storage, direct web-based data access and a REST API for integrating external software. The Uploader provides automated on-site anonymisation, encryption and data upload. Gateways provide a seamless process for uploading medical data from clinical systems to the research server. Results: GIFT-Cloud has been implemented in a multi-centre study for fetal medicine research. We present a case study of placental segmentation for pre-operative surgical planning, showing how GIFT-Cloud underpins the research and integrates with the clinical workflow. Conclusions: GIFT-Cloud simplifies the transfer of imaging data from clinical to research institutions, facilitating the development and validation of medical research software and the sharing of results back to the clinical partners. GIFT-Cloud supports collaboration between multiple healthcare and research institutions while satisfying the demands of patient confidentiality, data security and data ownership.