de Jong, BasBelar, OihanaBereciartua, ArantzaPicon, ArtzaiMuñoz, ElenaSevilla, D.Moscone, F.Gandon, FabienneTosseti, E.García, S.Riegman, PeterBilbao, Roberto2016-06-072016-06-072013-05Proceedings of ISBER 2013 Annual Meetingshttp://hdl.handle.net/11556/242Background: Pathology departments and biobanks are increasingly using Digital Pathology (DP) images for sharing of research results, ring trials, education, fast second-opinion diagnostics, pathology panels, digital back-up of slides, image analysis algorithms, and etcetera. To fully exploit the potential of DP, the BIOPOOL project develops software for extracting and gathering DP slides with well defined associated data from multiple biobanks and pathology archives to create pools of images, as biobanks networks, on which clinicians and researchers can search for reference, score for similarities with their own images using an innovative Content Based Image Retrieval system, and perform indepth image analyses. Methods: The BIOPOOL Proof-of-Concept (PoC) with minimal, critical functionality serves as the basis on which the system will be further developed. For this PoC we are studying existing DP image formats and systems that could be of use, designed both PoC and end-phase validation plans and end-phase functional requirements. Results: For the PoC, only colon DP slides with associated data (normal and high grade carcinoma), digitalised on Hamamatsu and Olympus scanners, are used. Pathologists have assigned morphological areas of interest for image searching development and creation of the basic DPpool, which were both validated. Functional requirements include a user-interface for searching on textual and morphology aspects, multiscanner format support, storage capacity, computational power for search processing and IT equipment and support. Conclusions: The PoC model is a template for expanding the BIOPOOL system to full functionality. After final validation BIOPOOL may then serve as a leading example for using the full potential of DP imaging.engconference outputopen accesscontent based image retrievalhistology imageimage processingcomputer vision