Extending XNAT towards a Cloud-based Quality Assessment Platform for Retinal Optical Coherence Tomographies

Christoph Jansen, Maximilian Beier, Michael Witt, Jie Wu, Dagmar Krefting

Abstract



Neuroscientific research is increasingly based on image analysis methods. Among them, optical coherence tomography (OCT) allows for non-invasive visualisation of anatomical structures on a micrometer scale. The platform presented in this paper is designed for automatic quality assessment of retinal OCTs. It extends the image management platform XNAT by services to calculate and store quality measures. It is also extensible regarding new quality measure algorithms, allowing the developer to upload, compile and test code for the system's architecture. The processing tools are provided as a cloud-based service employing OpenStack. Different approaches using fully equipped virtual machines and Docker containers are investigated and compared regarding security and performance aspects.

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