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

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

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.

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Acknowledgments

This study was funded by the FP7 Marie Curie Initial Training Network “MultiScaleHuman”: Multi-scale Biological Modalities for Physiological Human Articulation (2011–2015), contract MRTN-CT-2011-289897. This work is also partially funded by the Project FAS—MEDIARE “Nuove metodologie di Imaging Diagnostico per patologie reumatiche”.

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Authors and Affiliations

  1. CNR-IMATI Genova, Via De Marini 6, Genova, Italy

    Imon Banerjee, Chiara Eva Catalano, Giuseppe Patané & Michela Spagnuolo

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  1. Imon Banerjee
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  2. Chiara Eva Catalano
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  3. Giuseppe Patané
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  4. Michela Spagnuolo
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Correspondence to Imon Banerjee.

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Conflict of interest

Imon Banerjee, Chiara Eva Catalano, Giuseppe Patané, Michela Spagnuolo declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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This article does not contain any identifiable patient’s information.

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Banerjee, I., Catalano, C.E., Patané, G. et al. Semantic annotation of 3D anatomical models to support diagnosis and follow-up analysis of musculoskeletal pathologies. Int J CARS 11, 707–720 (2016). https://doi.org/10.1007/s11548-015-1327-6

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  • DOI: https://doi.org/10.1007/s11548-015-1327-6

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