Semantic annotation of 3D anatomical models to support diagnosis and follow-up analysis of musculoskeletal pathologies

Purpose: While 3D patient-specific digital models are currently available, thanks to advanced medical acquisition devices, there is still a long way to go before these models can be used in clinical practice. The goal of this paper is to demonstrate how 3D patient-specific models of anatomical parts can be analysed and documented accurately with morphological information extracted automatically from the data. Part-based semantic annotation of 3D anatomical models is discussed as a basic approach for sharing and reusing knowledge among clinicians for next-generation CAD-assisted diagnosis and treatments. Methods: We have developed (1) basic services for the analysis of 3D anatomical models and (2) a methodology for the enrichment of such models with relevant descriptions and attributes, which reflect the parameters of interest for medical investigations. The proposed semantic annotation is ontology-driven and includes both descriptive and quantitative labelling. Most importantly, the developed methodology permits to identify and annotate also parts-of-relevance of anatomical entities. Results: The computational tools for the automatic computation of qualitative and quantitative parameters have been integrated in a prototype system, the SemAnatomy3D framework, which demonstrates the functionalities needed to support effective annotation of 3D patient-specific models. From the first evaluation, SemAnatomy3D appears as an effective tool for clinical data analysis and opens new ways to support clinical diagnosis. Conclusions: The SemAnatomy3D framework integrates several functionalities for 3D part-based annotation. The idea has been presented and discussed for the case study of rheumatoid arthritis of carpal bones; however, the framework can be extended to support similar annotations in different clinical applications.