Towards an ontology for sharing medical images and regions of interest in neuroimaging

Temal, Lynda; Dojat, Michel; Kassel, Gilles; Gibaud, Bernard

doi:10.1016/j.jbi.2008.03.002

Cited by 57 publications

(37 citation statements)

References 21 publications

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“…Some ontologies have already been designed and used by the neuroscience and neuroimaging communities (Temal et al, 2008). Therefore, defining the neuroimaging classification on existing organized knowledge seems relevant.…”

Section: Domain Classification For Neuroimagingmentioning

confidence: 99%

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

et al. 2017

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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 BIOMedical Imaging SemanTic data management (BIOMIST) project aims at creating such a framework, yet with a radically different approach: the key insight behind it is the realization 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 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 method was then designed to handle workflows of processing chains in PLM. Following these principles, workflows are parameterized 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 magnetic resonance imaging-Share cohort acquisition of 2,000 participants.

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Section: Domain Classification For Neuroimagingmentioning

confidence: 99%

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

et al. 2017

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Section: Definition Of Neuroimaging Domain Classificationmentioning

confidence: 99%

PLM as a strategy for the management of heterogeneous information in bio-medical imaging field

Eynard

Joliot

Boutinaud

et al. 2017

IJITM

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Abstract:The amount of heterogeneous information that researchers in bio-medical imaging (BMI) field have to manage has grown significantly, and their costs remain high. Large-scale sharing and reusing of this information has become unavoidable. Some data management systems have been developed in neuroimaging field, however they miss to integrate the data provenance all along the research works, from study specifications to scientific publication. The manufacturing industry was confronted to similar issues twenty years ago and designed product lifecycle management (PLM) systems to properly share and manage product information all along its lifecycle and among project teams. Therefore, PLM systems are proposed to be a relevant strategy to manage BMI research studies information. The generic, flexible and PLM-oriented data model called BMI-lifecycle management (BMI-LM) is described, as well as a neuroimaging classification which brings flexibility to the information management system. A test implementation into a PLM system is presented, and the feedback from the GIN researchers is discussed.Keywords: bio-medical imaging; BMI; product lifecycle management; PLM; information management; heterogeneous information; data model.Reference to this paper should be made as follows: Allanic, M., Hervé, P-Y., Durupt, A., Joliot, M., Boutinaud, P. and Eynard, B. (2017) 'PLM as a strategy for the management of heterogeneous information in bio-medical imaging field', Int.

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“…An application ontology describes the relationship between concepts based on specific tasks (Liu et al, 2010). This can be seen from its application in courseware management for teachers (Jin et al, 2005), indexing (Assali et al, 2007), OntoNeuroBase which is used as a common semantic reference for aligning heterogeneous data in neuroimaging (Temal et al, 2008), smart homes (Chen et al, 2009), comparing UML elements type in class diagrams (Robleset al, 2012) and intrusion detection system (Meneses et al, 2015).…”

Section: Application Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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Towards an ontology for sharing medical images and regions of interest in neuroimaging

Cited by 57 publications

References 21 publications

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

BIOMIST: A Platform for Biomedical Data Lifecycle Management of Neuroimaging Cohorts

PLM as a strategy for the management of heterogeneous information in bio-medical imaging field

Towards knowledge modeling and manipulation technologies: A survey

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