Ontology-based Image Navigation: Exploring 3.0-T MR Neurography of the Brachial Plexus Using AIM and RadLex

Wang, Kenneth C.; Salunkhe, Aditya R; Morrison, James R.; Lee, Pearlene P.; Mejino, José L. V.; Detwiler, Landon T.; Brinkley, James F.; Siegel, Eliot L.; Rubin, Daniel L.; Carrino, John A.

doi:10.1148/rg.351130072

Cited by 9 publications

(12 citation statements)

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“…FMA IDs are either used directly in other ontologies and terminologies, or are declared to be equivalent to existing IDs. FMA IDs are also often used for data annotation, as for example regions corresponding to anatomical structures in 2-D or 3-D images (18). Because the FMA was initially in Frames and not open source, many other ontologies and terminologies invented their own anatomy axes, with the result that these ontologies and data are annotated with IDs that are different than those provided by the FMA, yet are semantically equivalent to them.…”

Section: Using the Converted Fmamentioning

confidence: 99%

From frames to OWL2: Converting the Foundational Model of Anatomy

Detwiler

Mejino

Brinkley

2016

Artificial Intelligence in Medicine

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Objective The Foundational Model of Anatomy (FMA) is an ontology that represents canonical anatomy at levels ranging from the entire body to biological macromolecules, and is rapidly become the primary reference ontology for human anatomy, and a template for model organisms. Prior to this work, the FMA was developed in a knowledge modeling language known as Protégé Frames. Frames is an intuitive representational language, but is no longer the industry standard. Recognizing the need for an official version of the FMA in the more modern semantic web language OWL2 (hereafter referred to as OWL), the objective of this work was to create a generalizable Frames-to-OWL conversion tool, to use the tool to convert the FMA to OWL, to “clean up” the converted FMA so that it classifies under an EL reasoner, and then to do all further development in OWL. Methods The conversion tool is a Java application that uses the Protégé knowledge representation API for interacting with the initial Frames ontology, and uses the OWL-API for producing new statements (axioms, etc.) in OWL. The converter is relation centric. The conversion is configurable, on a property-by-property basis, via user-specifiable XML configuration files. The best conversion, for each property, was determined in conjunction with the FMA knowledge author. The convertor is potentially generalizable, which we partially demonstrate by using it to convert our Ontology of Craniofacial Development and Malformation as well as the FMA. Post-conversion cleanup involved using the Explain feature of Protégé to trace classification errors under the ELK reasoner in Protégé, fixing the errors, then re-running the reasoner. Results We are currently doing all our development in the converted and cleaned-up version of the FMA. The FMA (updated every 3 months) is available via our FMA web page http://si.washington.edu/projects/fma, which also provides access to mailing lists, an issue tracker, a SPARQL endpoint (updated every week), and an online browser. The converted OCDM is available at http://www.si.washington.edu/projects/ocdm. The conversion code is open source, and available at http://purl.org/sig/software/frames2owl. Prior to the post-conversion cleanup 73% of the more than 100,000 classes were unsatisfiable. After correction of six types of errors no classes remained unsatisfiable. Conclusion Because our FMA conversion captures all or most of the information in the Frames version, is the only complete OWL version that classifies under an EL reasoner, and is maintained by the FMA authors themselves, we propose that this version should be the only official release version of the FMA in OWL, supplanting all other versions. Although several issues remain to be resolved post-conversion, release of a single, standardized version of the FMA in OWL will greatly facilitate its use in informatics research and in the development of a global knowledge base within the semantic web. Because of the fundamental nature of anatomy in both understanding and organizing biomedical infor...

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Section: Using the Converted Fmamentioning

confidence: 99%

From frames to OWL2: Converting the Foundational Model of Anatomy

Detwiler

Mejino

Brinkley

2016

Artificial Intelligence in Medicine

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“…It extends a developed ontology that allows sharing brain images and region of interest in neuroimaging. In [7] Wang et al developed an application designed for users for ontology-based image navigation. It relies on the radiology knowledge source RadLex (Radiology Lexicon) [8] and the Annotation and Image Markup standard [9] and is applied to the context of magnetic resonance imaging of the brachial plexus.…”

Section: Introductionmentioning

confidence: 99%

Efficient Results in Semantic Interoperability for Health Care

Soualmia

Charlet

2016

Yearb Med Inform

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SummaryObjectives: To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. Method: We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. Results: The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic

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“…However, the peripheral nervous system (PNS)-nerves, ganglia, and roots-continued to be considered "difficult to assess" by radiologists ( 1 ) . The PNS has been studied through invasive tests, such as electrodiagnostic studies and nerve biopsies, which cause patient discomfort ( 2 , 3 ) . In this context, the development of magnetic fields that were more powerful, which allowed the acquisition of fine sequences with high resolution, made it possible to study the PNS by MR neurography, the first of such studies being published in 1993 ( 1 ) .…”

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confidence: 99%

“…The indication for MR neurography will define its length and duration. MR neurography is more efficacious than are electrodiagnostic studies in the evaluation of deep structures ( 1 ) , the main indications being related to inflammatory/genetic/immune-mediated diseases of the peripheral nerves, neural tumors, or posttraumatic alterations ( 3 , 5 ) . If the neuropathy is extensive, as in cases of Charcot-Marie-Tooth disease, MR neurography can cover all the nerve trunks, whereas directed sequences can be employed in the evaluation of localized lesions, such as a sciatic compression caused by lumbar disc herniation.…”

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confidence: 99%

Neurography - a new look at the peripheral nervous system

Lopes

2017

Radiol Bras

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Ontology-based Image Navigation: Exploring 3.0-T MR Neurography of the Brachial Plexus Using AIM and RadLex

Cited by 9 publications

References 41 publications

From frames to OWL2: Converting the Foundational Model of Anatomy

From frames to OWL2: Converting the Foundational Model of Anatomy

Efficient Results in Semantic Interoperability for Health Care

Neurography - a new look at the peripheral nervous system

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