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Item SemAnatomy3D: Annotation of Patient-Specific Anatomy(The Eurographics Association, 2015) Banerjee, Imon; Patanè, Giuseppe; Spagnuolo, Michela; Andrea Giachetti and Silvia Biasotti and Marco TariniIn the digital age of medicine, patient-specific 3D anatomical reconstructions are becoming increasingly relevant in several applications, starting from bio-mechanical simulation, virtual surgery, implant design to computerassisted diagnosis. While problems related to imaging and 3D reconstruction have been largely resolved by the advancement in technologies, tools for extracting, coding, sharing and retrieving the semantic content of the patient-specific 3D models are still far from being satisfactory. In this context, we propose SemAnatomy3D framework that aims to bridge the semantic gap between patient-specific 3D geometry and formalized domain knowledge for making the semantics more usable for the definition of patient-specific atlas of anatomy. The purpose of this paper is to describe primary components of the framework. We specialized our framework for the carpal region, but, in principle, it can support similar tasks for other anatomical districts.Item Grontocrawler: Graph-Based Ontology Exploration(The Eurographics Association, 2015) Agibetov, Asan; Patanè, Giuseppe; Spagnuolo, Michela; Andrea Giachetti and Silvia Biasotti and Marco TariniBiomedical ontologies helps discover hidden semantic links between heterogeneous and multi-scale biomedical datasets. Computational methods to ontology analysis may provide a semantic flavor to data analysis of biomedical mathematical models and help discover hidden links. In this paper we present Grontocrawler - a framework for visual ontology exploration applied to the biomedical domain. We define an OWL sublanguage - L and we present a methodology for transformation of L ontologies into directed labelled graphs. We then show how Social Network Analysis techniques (e.g., centrality measures, graph partitioning, community detection) can be used to i) filter the information presented to the user, and ii) provide a summary of knowledge encoded in the ontology. Finally, we show the application of ontology exploration in the biomedical domain to help discover hidden links between the biomedical datasets.