GIFT-Cloud: A data sharing and collaboration platform for medical imaging research

Tom Doel; Dzhoshkun I Shakir; Rosalind Pratt; Michael Aertsen; James Moggridge; Erwin Bellon; Anna L David; Jan Deprest; Tom Vercauteren; Sébastien Ourselin

doi:10.1016/j.cmpb.2016.11.004

GIFT-Cloud: A data sharing and collaboration platform for medical imaging research

Comput Methods Programs Biomed. 2017 Feb:139:181-190. doi: 10.1016/j.cmpb.2016.11.004. Epub 2016 Nov 10.

Authors

Affiliations

¹ Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK. Electronic address: t.doel@ucl.ac.uk.
² Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK.
³ Translational Imaging Group, Centre for Medical Imaging Computing, University College London, London, UK; Institute for Women's Health, University College London, London, UK.
⁴ Department of Imaging & Pathology, UZ Leuven, Leuven, Belgium.
⁵ Department of Medical Physics, UCL Hospitals, London, UK.
⁶ Department of Imaging & Pathology, UZ Leuven, Leuven, Belgium; Department of Information Technology, UZ Leuven, Leuven, Belgium.
⁷ Institute for Women's Health, University College London, London, UK.
⁸ Institute for Women's Health, University College London, London, UK; Department of Obstetrics, UZ Leuven, Leuven, Belgium.

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.

Keywords: Anonymisation; Biomedical research; Cross-disciplinary research; Data sharing; Deidentification; Fetal surgery.

MeSH terms

Cloud Computing*
Computer Security
Cooperative Behavior*
Diagnostic Imaging*
Hospital Administration
Information Dissemination*
Universities / organization & administration

Grants and funding

MR/J01107X/1/MRC_/Medical Research Council/United Kingdom