Foundational Model Of Anatomy Research Articles

Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and Validation.

BackgroundAs the manual creation and maintenance of biomedical ontologies are labor-intensive, automatic aids are desirable in the lifecycle of ontology development.ObjectiveProvided with a set of concept names in the Foundational Model of Anatomy (FMA), we propose an innovative method for automatically generating the taxonomy and the partonomy structures among them, respectively.MethodsOur approach comprises 2 main tasks: The first task is predicting the direct relation between 2 given concept names by utilizing word embedding methods and training 2 machine learning models, Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory Networks (Bi-LSTM). The second task is the introduction of an original granularity-based method to identify the semantic structures among a group of given concept names by leveraging these trained models.ResultsResults show that both CNN and Bi-LSTM perform well on the first task, with F1 measures above 0.91. For the second task, our approach achieves an average F1 measure of 0.79 on 100 case studies in the FMA using Bi-LSTM, which outperforms the primitive pairwise-based method.ConclusionsWe have investigated an automatic way of predicting a hierarchical relationship between 2 concept names; based on this, we have further invented a methodology to structure a group of concept names automatically. This study is an initial investigation that will shed light on further work on the automatic creation and enrichment of biomedical ontologies.

Biomedical ontology alignment: an approach based on representation learning

BackgroundWhile representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.ResultsAn ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.ConclusionsOur proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange-Based Prior Computed Tomography (CT) Alerting System.

BackgroundA health information exchange (HIE)–based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions). Notification of previous same studies across an HIE requires mapping of local site CT codes to a standard terminology for exam names (such as Logical Observation Identifiers Names and Codes [LOINC]) to show that two studies with different local codes and descriptions are equivalent. Notifying of prior similar or proximate CTs requires an additional mapping of exam codes to anatomic regions, ideally coded by an anatomic terminology. Several anatomic terminologies exist, but no prior studies have evaluated how well they would support an alerting use case.ObjectiveThe aim of this study was to evaluate the fitness of five existing standard anatomic terminologies to support similar or proximate alerts of an HIE-based prior CT alerting system.MethodsWe compared five standard anatomic terminologies (Foundational Model of Anatomy, Systematized Nomenclature of Medicine Clinical Terms, RadLex, LOINC, and LOINC/Radiological Society of North America [RSNA] Radiology Playbook) to an anatomic framework created specifically for our use case (Simple ANatomic Ontology for Proximity or Similarity [SANOPS]), to determine whether the existing terminologies could support our use case without modification. On the basis of an assessment of optimal terminology features for our purpose, we developed an ordinal anatomic terminology utility classification. We mapped samples of 100 random and the 100 most frequent LOINC CT codes to anatomic regions in each terminology, assigned utility classes for each mapping, and statistically compared each terminology’s utility class rankings. We also constructed seven hypothetical alerting scenarios to illustrate the terminologies’ differences.ResultsBoth RadLex and the LOINC/RSNA Radiology Playbook anatomic terminologies ranked significantly better (P<.001) than the other standard terminologies for the 100 most frequent CTs, but no terminology ranked significantly better than any other for 100 random CTs. Hypothetical scenarios illustrated instances where no standard terminology would support appropriate proximate or similar alerts, without modification.ConclusionsLOINC/RSNA Radiology Playbook and RadLex’s anatomic terminologies appear well suited to support proximate or similar alerts for commonly ordered CTs, but for less commonly ordered tests, modification of the existing terminologies with concepts and relations from SANOPS would likely be required. Our findings suggest SANOPS may serve as a framework for enhancing anatomic terminologies in support of other similar use cases.

A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning

The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan's construction can affect the therapy outcome.

From frames to OWL2: Converting the Foundational Model of Anatomy.

The Foundational Model of Anatomy (FMA) [Rosse C, Mejino JLV. A reference ontology for bioinformatics: the Foundational Model of Anatomy. J. Biomed. Inform. 2003;36:478-500] is an ontology that represents canonical anatomy at levels ranging from the entire body to biological macromolecules, and has 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. 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. 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. 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 information, and because of the importance of the FMA in particular in representing human anatomy, the FMA in OWL should greatly accelerate the development of an anatomically based structural information framework for organizing and linking a large amount of biomedical information.

Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study.

Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called “Human Connectomics Ontology” (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain. First a neuroanatomical “view” called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as “MR_Node” and “MR_Route”) and object properties (such as “tracto_connects”) pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries. This approach can facilitate comparison of data across scales, modalities and species.

A Rational Informatics-enabled approach to Standardised Nomenclature of Contours and Volumes in Radiation Oncology Planning

The standardising of nomenclature in the radiotherapy planning process has deep implications for the abilityof the profession to examine the adequacy of construction of radiotherapy plans in outcomes research, particularly in relation to disease control and toxicity generation. This paper proposes an interim standardisednomenclature which can be used by any institution as a template for a mappable local standard.The nomenclature is systematically constructed using the Foundational Model of Anatomy, ICRU Report 50 and ICRU report 62. The system foreshadows a XML metadata structure to detail the method of constructionof volumes. Treatment Planning System vendors should build their software with the ability to use this systematic construction technique so that contours and volumes in a radiotherapy plan can be annotated. Thismetadata will allow the investigation of how a radiation plan's construction can a ect the therapy outcome.

Neuroanatomical domain of the foundational model of anatomy ontology

BackgroundThe diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging.DescriptionThe Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications.ConclusionsA requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models.

My Corporis Fabrica: an ontology-based tool for reasoning and querying on complex anatomical models.

BackgroundMultiple models of anatomy have been developed independently and for different purposes. In particular, 3D graphical models are specially useful for visualizing the different organs composing the human body, while ontologies such as FMA (Foundational Model of Anatomy) are symbolic models that provide a unified formal description of anatomy. Despite its comprehensive content concerning the anatomical structures, the lack of formal descriptions of anatomical functions in FMA limits its usage in many applications. In addition, the absence of connection between 3D models and anatomical ontologies makes it difficult and time-consuming to set up and access to the anatomical content of complex 3D objects.ResultsFirst, we provide a new ontology of anatomy called My Corporis Fabrica (MyCF), which conforms to FMA but extends it by making explicit how anatomical structures are composed, how they contribute to functions, and also how they can be related to 3D complex objects. Second, we have equipped MyCF with automatic reasoning capabilities that enable model checking and complex queries answering. We illustrate the added-value of such a declarative approach for interactive simulation and visualization as well as for teaching applications.ConclusionsThe novel vision of ontologies that we have developed in this paper enables a declarative assembly of different models to obtain composed models guaranteed to be anatomically valid while capturing the complexity of human anatomy. The main interest of this approach is its declarativity that makes possible for domain experts to enrich the knowledge base at any moment through simple editors without having to change the algorithmic machinery. This provides MyCF software environment a flexibility to process and add semantics on purpose for various applications that incorporate not only symbolic information but also 3D geometric models representing anatomical entities as well as other symbolic information like the anatomical functions.

The ontology of craniofacial development and malformation for translational craniofacial research

We introduce the Ontology of Craniofacial Development and Malformation (OCDM) as a mechanism for representing knowledge about craniofacial development and malformation, and for using that knowledge to facilitate integrating craniofacial data obtained via multiple techniques from multiple labs and at multiple levels of granularity. The OCDM is a project of the NIDCR-sponsored FaceBase Consortium, whose goal is to promote and enable research into the genetic and epigenetic causes of specific craniofacial abnormalities through the provision of publicly accessible, integrated craniofacial data. However, the OCDM should be usable for integrating any web-accessible craniofacial data, not just those data available through FaceBase. The OCDM is based on the Foundational Model of Anatomy (FMA), our comprehensive ontology of canonical human adult anatomy, and includes modules to represent adult and developmental craniofacial anatomy in both human and mouse, mappings between homologous structures in human and mouse, and associated malformations. We describe these modules, as well as prototype uses of the OCDM for integrating craniofacial data. By using the terms from the OCDM to annotate data, and by combining queries over the ontology with those over annotated data, it becomes possible to create "intelligent" queries that can, for example, find gene expression data obtained from mouse structures that are precursors to homologous human structures involved in malformations such as cleft lip. We suggest that the OCDM can be useful not only for integrating craniofacial data, but also for expressing new knowledge gained from analyzing the integrated data.

Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search.

In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset.

An analysis of FMA using structural self-bisimilarity

As ontologies are mostly manually created, they tend to contain errors and inconsistencies. In this paper, we present an automated computational method to audit symmetric concepts in ontologies by leveraging self-bisimilarity and linguistic structure in the concept names. Two concepts A and B are symmetric if concept B can be obtained from concept A by replacing a single modifier such as “left” with its symmetric modifier such as “right.” All possible local structural types for symmetric concept pairs are enumerated according to their local subsumption hierarchy, and the pairs are further classified into Non-Matches and Matches. To test the feasibility and validate the benefits of this method, we computed all the symmetric modifier pairs in the Foundational Model of Anatomy (FMA) and selected six of them for experimentation. 9893 Non-Matches and 221 abnormal Matches with potential errors were discovered by our algorithm. Manual evaluation by FMA domain experts on 176 selected Non-Matches and all the 221 abnormal Matches found 102 missing concepts and 40 misaligned concepts. Corrections for them have currently been implemented in the latest version of FMA. Our result demonstrates that self-bisimilarity can be a valuable method for ontology quality assurance, particularly in uncovering missing concepts and misaligned concepts. Our approach is computationally scalable and can be applied to other ontologies that are rich in symmetric concepts.

OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform

Event Abstract Back to Event OntoCATI: Towards an ontology of neuroimaging measures in the CATI Platform Lydie EDWARD1, 2*, Grégory Operto2, 3, Sylvain Poret1, 2, Yann Cointepas2, 3, Nader Cheaib2, 3, Leila Makkaoui2, 3 and Bénédicte M. Batrancourt1, 2 1 CRICM UPMC/INSERM UMR_S975, France 2 CATI, France 3 CEA, UNATI/NeuroSpin/I2BM, France CATI (Acquisition Centre and Image Processing, a French national platform supported by the Alzheimer’s Foundation [1]) provides software solutions for automatic measurement of different biomarkers related to specific modalities (Figure 1). CATI provides assistance for acquiring, analyzing, organizing and sharing neuroimaging data in the context of multi-centric clinical research studies. Our goal is to support the scientific study of the different working groups (neurologist, MRI PET physician, quality check operations engineer, engineer developing brain imaging tools) and help them to model the knowledge linked to their domain of interest. We proposed a structured metadata hierarchy for storing information relevant to various aspects of a project (e.g. study, subject, dataset, etc.) along with derived data and process in order to develop improved semantic request and reasoning tools for knowledge extraction on the derived data (for example we would like to compare the hippocampal volumes obtained on different time points). We developed a database schema CATISchema and an ontology OntoCATI [2]. At this stage, the first module of OntoCATI supports the information concerning acquisition, raw data (clinical and neuroimaging), and image processing. This module uses DOLCE [3] as its upper ontology. The image processing generates a multitude of data and derived measures. These measures are potential biomarkers for the Alzheimer’s disease (AD). In order to build the second module of OntoCATI dedicated to these measurements we first focus on the data issued from the modality T1 MRI and on three types of measures: rate of hippocampal atrophy with the longitudinal version of SACHA [4], cortical thickness reduction with Freesurfer (http://ftp.nmr.mgh.harvard.edu/) and opening of primary cortical folds using the longitudinal version of Morphologist-2012 pipeline [5]. An important point in our modeling consists in distinguishing the concept of measure and biomarker. For example, hippocampal volume is considered as the most widely used and admitted biomarkers in the AD to monitor its progression [6]. Therefore in OntoCATI this measure belongs to the sub-class of biomarker. Concerning the other measures the ontology will allow us to determine what measures become biomarkers using queries. To model the different measurements that are the assignment of numbers to anatomical entities, we need to describe the brain anatomy (e.g., hippocampus), the measure (magnitude, dimensions (units) and uncertainty) and the disease concepts and their codification. This module of OntoCATI uses the Basic Formal Ontology (BFO) as its upper level ontology [7]. For the anatomy domain we refer to the Foundational Model of Anatomy ontology (FMA) [8], which adopts and extends into BFO [9], a domain-independent, spatio-temporal theory that provides a rigorous ontological framework. Concerning the pathology domain we rely on ICD-10 [10] and UMLS standard [11]. For example to describe the hippocampus, according to FMA, we define hippocampus as a ‘gyrus of limbic lobe' that is a ‘segment of cerebral hemisphere’ that is a subClass of ‘Anatomical Entity’ that is a subclass of the BFO concept 'independent continuant'. To represent the volume of the hippocampus we create the relation has for volume that is linking the concept of ‘hippocampus’ to the BFO concept ‘three dimensional region’ (Figure 2). This is the first step in our work. In the future, we will integrate other concepts to represent the other measures (cortical thickness, opening of primary cortical folds, etc.). We will also work on the other modalities presented in Figure 1 and we will develop the queries to cross the frontier between measures and biomarkers. Figure 1 Figure 2 Acknowledgements This work is funded by the French Foundation Plan Alzheimer and was realized in the CATI study group. The authors gratefully acknowledge all contributors.

The Foundational Model of Anatomy in OWL 2 and its use

The objective is to represent the Foundational Model of Anatomy (FMA) in the OWL 2 Web Ontology Language (informally OWL 2), and to use it in a European cross-lingual portal of health terminologies for indexing and searching Web resources. Formalizing the FMA in OWL 2 is essential for semantic interoperability, to improve its design, and to ensure its reliability and correctness, which is particularly important for medical applications. The native FMA was implemented in frames and stored in a MySQL database backend. The main strength of the method is to leverage OWL 2 expressiveness and to rely on the naming conventions of the FMA, to make explicit some implicit semantics, while improving its ontological model and fixing some errors. Doing so, the semantics (meaning) of the formal definitions and axioms are anatomically correct. A flexible tool enables the generation of a new version in OWL 2 at each Protégé FMA update. While it creates by default a 'standard' version of the FMA in OWL 2 (FMA-OWL), many options allow for producing other variants customized to users' applications. Once formalized in OWL 2, it was possible to use an inference engine to check the ontology and detect inconsistencies. Next, the FMA-OWL was used to derive a lightweight FMA terminology for a European cross-lingual portal of terminologies/ontologies for indexing and searching resources. The transformation is mainly based on a reification process. Complete representations of the entire FMA in OWL 1 or OWL 2 are now available. The formalization tool is flexible and easy to use, making it possible to obtain an OWL 2 version for all existing public FMA. A number of errors were detected in the native FMA and several patterns of recurrent errors were identified in the original FMA. This shows how the underlying OWL 2 ontology is essential to ensure that the lightweight derived terminology is reliable. The FMA OWL 2 ontology has been applied to derive an anatomy terminology that is used in a European cross-lingual portal of health terminologies. This portal is daily used by librarians to index Web health resources. In August 2011, 6481 out of 81,450 health resources of CISMeF catalog (http://www.chu-rouen.fr/cismef/--accessed 29.08.12) (7.96%) were indexed with at least one FMA entity. The FMA is a central terminology used to index and search Web resources. To the best of our knowledge, neither a complete representation of the entire FMA in OWL 2, nor an anatomy terminology available in a cross-lingual portal, has been developed to date. The method designed to represent the FMA ontology in OWL 2 presented in this article is general and may be extended to other ontologies. Using a formal ontology for quality assurance and deriving a lightweight terminology for biomedical applications is a general and promising strategy.

Um modelo para a visualização do conhecimento baseado em arquétipos visuais

This paper presents a model that aims to facilitate the visualization of the knowledge stored in digital repositories using visual archetypes. Archetypes are structures that contain visual representations of the real world that are known a priori by the target group, and which have semantic structures for identifying the entities of the domain represented in each region. The proposed model is supported by the framework for knowledge visualization proposed by Burkhard and describes the users’ interactions with visual archetypes. The user through the archetypes can retrieve and view the knowledge related to the entities represented in the archetypes’ images. A prototype was developed to demonstrate the feasibility of the model using archetypes in the biomedical field, the Foundational Model of Anatomy and the Unified Medical Language System as domain knowledge and the Scientific Electronic Library Online database as a document repository. The use of visual representations in archetypes facilitates the dissemination of knowledge, because these are part of the world view of users and can easily be related with prior knowledge. Visual representations are processed quickly in the brain and require less effort than the processing of textual information.

Ontological labels for automated location of anatomical shape differences

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

Systematic identification and correction of spelling errors in the Foundational Model of Anatomy

We describe a method for automating the detection and correction of spelling errors in the Foundational Model of Anatomy (FMA). The FMA was tokenized into 4893 distinct words; misspellings were identified and corrected using the National Library of Medicine's SPECIALIST GSpell Spelling Suggestion API. We identified 43 errors occurring in 97 terms, and 6 words of questionable or inconsistent spelling occurring in 26 terms. These errors are replicated in other reference terminologies that use the FMA. Our approach may be useful as part of a quality assurance process for other large-scale biomedical knowledge resources.

Spatial-symbolic Query Engine in Anatomy

Currently, the primary means for answering anatomical questions such as 'what vital organs would potentially be impacted by a bullet wound to the abdomen?' is to look them up in textbooks or to browse online sources. In this work we describe a semantic web service and spatial query processor that permits a user to graphically pose such questions as joined queries over separately defined spatial and symbolic knowledge sources. Spatial relations (e.g. anterior) were defined by two anatomy experts, and based on a 3-D volume of labeled images of the thorax, all the labeled anatomical structures were queried to retrieve the target structures for every query structure and every spatial relation. A web user interface and a web service were designed to relate existing symbolic information from the Foundational Model of Anatomy ontology (FMA) with spatial information provided by the spatial query processor, and to permit users to select anatomical structures and define queries. We evaluated the accuracy of results returned by the queries, and since there is no independent gold standard, we used two anatomy experts' opinions as the gold standard for comparison. We asked the same experts to define the gold standard and to define the spatial relations. The F-measure for the overall evaluation is 0.90 for rater 1 and 0.56 for rater 2. The percentage of observed agreement is 99% and Cohen's kappa coefficient reaches 0.51. The main source of disagreement relates to issues with the labels used in the dataset, and not with the tool itself. In its current state the system can be used as an end-user application but it is likely to be of most use as a framework for building end-user applications such as displaying the results as a 3-D anatomical scene. The system promises potential practical utility for obtaining and navigating spatial and symbolic data.

Translating the foundational model of anatomy into french using knowledge-based and lexical methods

BackgroundThe Foundational Model of Anatomy (FMA) is the reference ontology regarding human anatomy. FMA vocabulary was integrated into the Health Multi Terminological Portal (HMTP) developed by CISMeF based on the CISMeF Information System which also includes 26 other terminologies and controlled vocabularies, mainly in French. However, FMA is primarily in English. In this context, the translation of FMA English terms into French could also be useful for searching and indexing French anatomy resources. Various studies have investigated automatic methods to assist the translation of medical terminologies or create multilingual medical vocabularies. The goal of this study was to facilitate the translation of FMA vocabulary into French.MethodsWe compare two types of approaches to translate the FMA terms into French. The first one is UMLS-based on the conceptual information of the UMLS metathesaurus. The second method is lexically-based on several Natural Language Processing (NLP) tools.ResultsThe UMLS-based approach produced a translation of 3,661 FMA terms into French whereas the lexical approach produced a translation of 3,129 FMA terms into French. A qualitative evaluation was made on 100 FMA terms translated by each method. For the UMLS-based approach, among the 100 translations, 52% were manually rated as "very good" and only 7% translations as "bad". For the lexical approach, among the 100 translations, 47% were rated as "very good" and 20% translations as "bad".ConclusionsOverall, a low rate of translations were demonstrated by the two methods. The two approaches permitted us to semi-automatically translate 3,776 FMA terms from English into French, this was to added to the existing 10,844 French FMA terms in the HMTP (4,436 FMA French terms and 6,408 FMA terms manually translated).

AnatomicalTerms.info: Heading for an online solution to the anatomical synonym problem hurdles in data‐reuse from the Terminologia Anatomica and the foundational model of anatomy and potentials for future development

The many synonyms for anatomical structures confuse medical students and complicate medical communication. Easily accessible translations would alleviate this problem. None of the presently available resources-Terminologia Anatomica (TA), digital terminologies such as the Foundational Model of Anatomy (FMA), and websites-are fully satisfactory to this aim. Internet technologies offer new possibilities to solve the problem. Several authors have called for an online TA. An online translation resource should be easily accessible, user-friendly, comprehensive, expandable, and its quality determinable. As first step towards this goal, we built a translation website that we named www.AnatomicalTerms.info, based on the database of the FMA. It translates between English, Latin, eponyms, and to a lesser extent other languages, and presently contains over 31,000 terms for 7,250 structures, covering 95% of TA. In addition, it automatically presents searches for images, documents and anatomical variations regarding the sought structure. Several terminological and conceptual issues were encountered in transferring data from TA and FMA into AnatomicalTerms.info, resultant from these resources' different set-ups (paper versus digital) and targets (machine versus human-user). To the best of our knowledge, AnatomicalTerms.info is unique in its combination of user-friendliness and comprehensiveness. As next step, wiki-like expandability will be added to enable open contribution of clinical synonyms and terms in different languages. Specific quality measures will be taken to strike a balance between open contribution and quality assurance. AnatomicalTerms.info's mechanism that "translates" terms to structures furthermore may enhance targeted searching by linking images, descriptions, and other anatomical resources to the structures.