OntoVIP: An Ontology for the Annotation of Object Models Used for Medical Image Simulation

Gibaud, Bernard; Forestier, Germain; Benoit-Cattin, Hugues; Cervenansky, Frédéric; Clarysse, Patrick; Friboulet, Denis; Gaignard, Alban; Hugonnard, Patrick; Lartizien, Carole; Liebgott, Hervé; Montagnat, Johan; Tabary, Joachim; Glatard, Tristan

doi:10.1109/hisb.2012.35

Cited by 8 publications

(11 citation statements)

References 29 publications

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“…It also focuses on searching similarity between terms by applying query expansion. Other works [39,40] include the medical image of the liver such as MRI or CT scans in the process of diagnosis and present a pure semantic description of the medical context. Table 2 shows a recapitulation of some related works.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment

et al. 2018

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Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).

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Section: Related Workmentioning

confidence: 99%

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment

et al. 2018

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“…But little works focus on Bio-Imaging field. (Gibaud et al, 2014) defined concepts used in Bio-Imaging like: Dataset, Processing, Investigators, Medical Image files, Equipment, subjects, etc. (Figure 4) Based on the data model BMI-LM and the classification in Teamcenter, we adapt this ontology to the logic use of information in database.…”

Section: Ontological Model Constructionmentioning

confidence: 99%

Sharing Knowledge in Daily Activity: Application in Bio-Imaging

Pham

Matta

Durupt

et al. 2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Our approach uses the ontology to facilitate the data querying of users in the domain Bio-Imaging where the data resources are heterogeneous and complex. The dependencies among data and the evolution of data resources challenge users (especially for non-technician users) in querying the right data. Ontology can be used to share the users' understanding about data relationships to all community as well as to trace the database evolution. As consequence, using ontology is a promising solution to facilitate the user's query making process and to enhance the query's results.

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“…VIP faces the issues of producing not only raw data, but also populating its simulated data repository with meaningful data. It thus needs to bridge the gap between provenance in technical simulation workflows and domain knowledge formalized with the OntoVIP domain ontology [10,12] (see section 4.1).…”

Section: The Vip Simulation Platformmentioning

confidence: 99%

“…Second, coarse-grained domain-specific provenance, representing concise domain-specific statements resulting from production rules relying on the VIP medical image simulation ontology. These produced statements will finally constitute "semantic experiment summaries" in which a minimal set of statements link together simulation experiment results to experiment parameters through the OntoVIP [10,12] domain ontology.…”

Section: Usage Scenariomentioning

confidence: 99%

“…We rely on the OntoVIP [10,12] ontology (section 4.1) to model the medical image simulation domain and the OWL-S [19] generic service ontology (section 4.2) to semantically annotate services composed into workflows. We also highlight the issue of ambiguous semantic service annotations and propose in this knowledge capture process, to pay a particular attention in distinguishing Role and Natural concepts when annotating service parameters (section 4.3).…”

Section: Knowledge Capture In E-science Workflowsmentioning

confidence: 99%

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Domain-specific summarization of Life-Science e-experiments from provenance traces

Gaignard¹,

Montagnat²,

Gibaud³

et al. 2014

Journal of Web Semantics

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Translational research in Life-Science nowadays leverages e-Science platforms to analyse and produce huge amounts of data. With the unprecedented growth of Life-Science data repositories, identifying relevant data for analysis becomes increasingly difficult. The instrumentation of e-Science platforms with provenance tracking techniques provide useful information from a data analysis process design or debugging perspective. However raw provenance traces are too massive and too generic to facilitate the scientific interpretation of data. In this paper, we propose an integrated approach in which Life-Science knowledge is (i) captured through domain ontologies and linked to Life-Science data analysis tools, and (ii) propagated through rules to produced data, in order to constitute human-tractable experiment summaries. Our approach has been implemented in the Virtual Imaging Platform (VIP) and experimental results show the feasibility of producing few domain-specific statements which opens new data sharing and repurposing opportunities in line with Linked Data initiatives.

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OntoVIP: An Ontology for the Annotation of Object Models Used for Medical Image Simulation

Cited by 8 publications

References 29 publications

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment

Ontology-Based Approach for Liver Cancer Diagnosis and Treatment

Sharing Knowledge in Daily Activity: Application in Bio-Imaging

Domain-specific summarization of Life-Science e-experiments from provenance traces

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