Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented. The proposed platform has been successfully launched and is being validated nowadays among the early adopters’ community of radiologists, clinicians, and medical imaging researchers.
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Conflict of interest
AAB and LMB are shareholders of QUIBIM SL, a company dedicated to the analysis of imaging biomarkers.
This article does not contain any studies with human participants or animals performed by any of the authors.
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Alberich-Bayarri, Á., Hernández-Navarro, R., Ruiz-Martínez, E. et al. Development of imaging biomarkers and generation of big data. Radiol med 122, 444–448 (2017). https://doi.org/10.1007/s11547-017-0742-x
- Imaging biomarkers
- Big data
- Image processing