The galen project

Rector, Alan L.; Nowlan, W. A.

doi:10.1016/0169-2607(94)90020-5

Cited by 85 publications

(30 citation statements)

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“…Ontology gathers content theories about the sorts of objects, properties of objects, and relations between objects that are possible in a specified domain of knowledge [8]. In the field of anatomy, it corresponds to the conceptualization of the structures and spaces that constitute the human body, which we extended to the cerebral cortex anatomy following the model proposed by the team of the Digital Anatomist program [6,35] or GALEN [33], and added the numeric aspect. As in other fields, neuroanatomists are compelled to standardize the terminology of their discipline.…”

Section: Discussionmentioning

confidence: 99%

“…Yet further specifications such as the definition of identity criteria for the concepts are required. The two leading projects regarding the symbolic representation of anatomy are Digital Anatomist [6,35] and GALEN (Generalized Architecture for Languages, Encyclopedias and Nomenclatures in medicine) [33]. They are representatives of a ''concept-based approach''.…”

Section: Introductionmentioning

confidence: 99%

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Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results

Dameron

Gibaud

Morandi

2004

Surgical and Radiologic Anatomy

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The human cerebral cortex anatomy describes the brain organization at the scale of gyri and sulci. It is used as landmarks for neurosurgery as well as localization support for functional data analysis or inter-subject data comparison. Existing models of the cortex anatomy either rely on image labeling but fail to represent variability and structural properties or rely on a conceptual model but miss the inner 3D nature and relations of anatomical structures. This study was therefore conducted to propose a model of sulco-gyral anatomy for the healthy human brain. We hypothesized that both numeric knowledge (i.e., image-based) and symbolic knowledge (i.e., concept-based) have to be represented and coordinated. In addition, the representation of this knowledge should be application-independent in order to be usable in various contexts. Therefore, we devised a symbolic model describing specialization, composition and spatial organization of cortical anatomical structures. We also collected numeric knowledge such as 3D models of shape and shape variation about cortical anatomical structures. For each numeric piece of knowledge, a companion file describes the concept it refers to and the nature of the relationship. Demonstration software performs a mapping between the numeric and the symbolic aspects for browsing the knowledge base.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results

Dameron

Gibaud

Morandi

2004

Surgical and Radiologic Anatomy

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“…Modeling anatomy has been pursued in several works such as Galen [21] or Digital Anatomist [22]. However, they are much focused on the general level of organs and do not provide detailed descriptions of specific organ structures.…”

Section: Brain Cortex Anatomy Ontologymentioning

confidence: 99%

Web Ontology Language Requirements w.r.t Expressiveness of Taxonomy and Axioms in Medicine

Golbreich

Dameron²,

Gibaud³

et al. 2003

Lecture Notes in Computer Science

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Abstract. An important issue is to know whether Web ontology languages, meet the expected requirements of expressiveness and reasoning. This paper aims at contributing to this question in evaluating and comparing several languages. After describing the needs of a Semantic Web in medicine, it analyses Protégé and DAML+OIL primitives on a concrete medical ontology, the brain cortex anatomy ontology. It draws conclusions about the requirements that a Web ontology language should meet for the representation of medical taxonomy and axioms. The expressiveness of DAML+OIL or OWL DL seems suited to describe the complex taxonomic knowledge. But rules are required for representing the deductive knowledge (dependencies between relations) and to support several tasks (ontology construction, maintenance, verification, query of heterogeneous distributed information sources). Finally, the paper evaluates the features of the next standard OWL and of an hybrid language CARIN-ALN with respect to these requirements.

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“…For medical applications, anatomy provides a common reference used to reason about pathology or localization of functional activity [2,3]. The Foundation Model of Anatomy (FMA) [4] and Galen [5] are two major conceptual models that provide a symbolic representation of human anatomy. However, neither of them provides a satisfactory representation of brain-cortex anatomy.…”

Section: Contextmentioning

confidence: 99%

Using semantic dependencies for consistency management of an ontology of brain–cortex anatomy

Dameron

Musen

Gibaud

2007

Artificial Intelligence in Medicine

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ContextA symbolic model allows developers to represent general knowledge about a domain and the meaning that is commonly associated with it. This knowledge can be used by itself (e.g., for teaching), or indirectly as a reference to process specific facts (e.g., to assist queries or data retrieval). In the latter case, symbolic models are perceived as a key feature to provide software assistance for tasks that now require domain-aware intervention by a human. As interoperability of these software applications is desirable, shared conceptual models, and specifically ontologies, play a major role in a Semantic Web context [1]. Since these models are to be usable by software, they must meet explicit representation and consistency requirements. For medical applications, anatomy provides a common reference used to reason about pathology or localization of functional activity [2,3]. The Foundation Model of Anatomy (FMA) [4] and Galen [5] are two major conceptual models that provide a symbolic representation of human anatomy. However, neither of them provides a satisfactory representation of brain-cortex anatomy. The major sources of neuroanatomical knowledge are paper-based atlases [6,7] and terminological systems such as Neuronames [8].We are working on an ontology of brain-cortex anatomy. Our goal is more to formalize existing knowledge than it is to propose new anatomical concepts or relationships. Our model has been described in previous publications [9,10]. It comprises 304 concepts and 1254 relationships that represent the organization of anatomical structures. Because the brain surface presents complicated folding patterns, typical anatomical structures are gyri (the bulges of cerebral matter, similar to hills), the sulci (the hollow foldings, similar to valleys) and lobes (sets of gyri). Our model's taxonomy hierarchy is composed of three levels. First, the generic level contains concepts such as Lobe or Sulcus, and is mainly used to define the domain and range of the relationships. Second, the abstract level represents a prototypical brain hemisphere, and contains concepts such as Frontal Lobe or Central Sulcus. Third, the lateralized level is used to represent left/right asymmetries, and contains concepts such as Left Frontal lobe. For mereology, the model identifies several relationships such as hasDirectAnatomicalPart, hasAnatomicalPart, hasSegment and their properties, inspired from previous theoretical works [11,12]. The model also represents neighborhood relationships such as the separation of two cortical structures by a sulcus, anatomical continuity, and sulci connection.In this context, managing the semantic consistency of the ontology has been one of our main concerns.

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The galen project

Cited by 85 publications

References 0 publications

Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results

Numeric and symbolic knowledge representation of cerebral cortex anatomy: methods and preliminary results

Web Ontology Language Requirements w.r.t Expressiveness of Taxonomy and Axioms in Medicine

Using semantic dependencies for consistency management of an ontology of brain–cortex anatomy

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