6 March 2018 Faster, efficient and secure collection of research images: the utilization of cloud technology to expand the OMI-DB
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Abstract
The demand for medical images for research is ever increasing owing to the rapid rise in novel machine learning approaches for early detection and diagnosis. The OPTIMAM Medical Image Database (OMI-DB)1,2 was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, annotations and expert-determined ground truths. Since the inception of the database in early 2011, the volume of images and associated data collected has dramatically increased owing to automation of the collection pipeline and inclusion of new sites. Currently, these data are stored at each respective collection site and synced periodically to a central store. This leads to a large data footprint at each site, requiring large physical onsite storage, which is expensive.

Here, we propose an update to the OMI-DB collection system, whereby the storage of all the data is automatically transferred to the cloud on collection. This change in the data collection paradigm reduces the reliance of physical servers at each site; allows greater scope for future expansion; and removes the need for dedicated backups and improves security. Moreover, with the number of applications to access the data increasing rapidly with the maturity of the dataset cloud technology facilities faster sharing of data and better auditing of data access. Such updates, although may sound trivial; require substantial modification to the existing pipeline to ensure data integrity and security compliance. Here, we describe the extensions to the OMI-DB collection pipeline and discuss the relative merits of the new system.
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M. N. Patel, M. N. Patel, K. Young, K. Young, M. D. Halling-Brown, M. D. Halling-Brown, } "Faster, efficient and secure collection of research images: the utilization of cloud technology to expand the OMI-DB", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105791E (6 March 2018); doi: 10.1117/12.2295020; https://doi.org/10.1117/12.2295020
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