BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts - Allanic, Marianne; Hervé, Pierre-Yves; Pham, Cong-Cuong; Lekkal, Myriam; Durupt, Alexandre; Brial, Thierry; Grioche, Arthur; Matta, Nada; Boutinaud, Philippe; Eynard, Benoit; Joliot, Marc (Frontiers in ICT, 2017)
The data management needs of the neuroimaging community are currently addressed by several specialized software platforms, which automate repetitive data import, archiving and processing tasks. The BIOMIST (BIOMedical Imaging SemanTic data management) project aims at creating such a framework, yet with a radically different approach: the key insight behind it is the realisation that the data management needs of the neuroimaging community – organizing the secure and convenient storage of large amounts of large files, bringing together data from different scientific domains, managing workflows and access policies, ensuring traceability and sharing data across different labs – are actually strikingly similar to those already expressed by the manufacturing industry. The BIOMIST neuroimaging data management framework is built around the same systems as those that were designed in order to meet the requirements of the industry. Product Lifecycle Management (PLM) systems rely on an object-oriented data model and allow the traceability of data and workflows throughout the life of a product, from its design to its manufacturing, maintenance and end of life, while guaranteeing data consistency and security. The BioMedical Imaging – Lifecycle Management (BMI-LM) data model was designed to handle the specificities of neuroimaging data in PLM systems, throughout the lifecycle of a scientific study. This data model is both flexible and scalable, thanks to the combination of generic objects and domain-specific classes sourced from publicly available ontologies. The Data Integrated Management and Processing (DIMP) method was then designed to handle workflows of processing chains in PLM. Following these principles, workflows are parameterised and launched from the PLM platform onto a computer cluster, and the results automatically return to the PLM where they are archived along with their provenance information. Third, to transform the PLM into a full-fledged neuroimaging framework, we developed a series of external modules: DICOM import, XML form data import web-services, flexible graphical querying interface, and SQL export to spreadsheets. Overall, the BIOMIST platform is well suited for the management of neuroimaging cohorts, and it is currently used for the management of the BIL&GIN dataset (300 participants) and the ongoing MRI-Share cohort acquisition of 2000 participants.