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

  • Imon BanerjeeEmail author
  • Chiara Eva Catalano
  • Giuseppe Patané
  • Michela Spagnuolo
Original Article



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.


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.


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.


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.


Semantic annotation Patient-specific 3D model Anatomical landmarks 3D morphological characterization Biomedical ontology 



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”.

Compliance with ethical standards

Conflict of interest

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

Ethical approval

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

Informed consent

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

Supplementary material

Supplementary material 1 (mp4 66102 KB)


  1. 1.
    Rosse C, Jose LV, Jr Mejino (2007) The foundational model of anatomy ontology. Anat Ontol Bioinform Princip Pract 6:59–117Google Scholar
  2. 2.
    Mejino JLV, Rubin DL, Brinkley JF (2008) FMA-RadLex: an application ontology of radiological anatomy derived from the foundational model of anatomy reference ontology. In: Proceedings of AMIA symposiumGoogle Scholar
  3. 3.
    Lia Z, Yanga MC, Ramani K (2009) A methodology for engineering ontology acquisition and validation. Artif Intell Eng Des Anal Manuf 23(1):37–51CrossRefGoogle Scholar
  4. 4.
    Rubin DL, Rodriguez C, Shah P, Beaulieu C (2008) iPad: semantic annotation and markup of radiological images. In: AMIA 2008 symposium proceedings, pp 626-630Google Scholar
  5. 5.
    OsiriX Dicom viewer. [Online].
  6. 6.
    Mongkolwat P, Kleper V, Talbot S, Rubin D (2014) The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model. J Digit Imaging 27(6):692–701CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Seiferta S, Kelma M, Moeller M, Mukherjee S, Cavallarod K (2010) Semantic annotation of medical images. Med Imaging 7628:2010Google Scholar
  8. 8.
    World Health Organization and others (1992) The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization, GenevaGoogle Scholar
  9. 9.
    Spagnuolo M, Falcidieno B (2009) 3D media and the semantic web. IEEE Intell Syst 24:90–96Google Scholar
  10. 10.
    Banerjee I, Catalano CE, Robbiano F, Spagnuolo M (2014) Accessing and representing knowledge in the medical field: visual and lexical modalities. In: Magnenat-Thalmann N, Ratib O, Choi HF (eds) 3D multiscale physiological human. Springer, London, pp 297-316Google Scholar
  11. 11.
    Mitsuhashi N, Fujieda K, Tamura T, Kawamoto S, Takagi T, Okubo K (2009) BodyParts3D: 3D structure database for anatomical concepts. Nucleic Acids Res 37:D782–D785CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Palombi O, Bousquet G, Jospin D, Reveret L, Faure F (2009) My corporis fabrica: a Unified ontological, geometrical and mechanical view of human anatomy. In: 2nd workshop on 3D physiological human, 3DPH2009, vol 5903. Zermatt, Switzerland, pp 207–219Google Scholar
  13. 13.
    Primal pictures. [Online].
  14. 14.
    Fedorova A, Beichelb R, Kalpathy-Cramerc J, Finetd J, Fillion-Robind J, Pujola S, Bauerb C, Jenningsc D, Fennessya F, Sonkab M, Buattib J, Aylwardd S, Millere JV, Pieperf S, Kikinisa R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341Google Scholar
  15. 15.
    Chiara Eva Catalano, Mortara Michela, Spagnuolo Michela, Falcidieno Bianca (2011) Semantics and 3D media: current issues and perspectives. Comput Gr 35:869–877CrossRefGoogle Scholar
  16. 16.
    Catalano CE, Robbiano F, Parascandolo P, Cesario L, Vosilla L, Barbieri F, Spagnuolo M, Viano G, Cimmino MA (2013) Exploiting 3D part-based analysis, description and indexing to support medical applications. Med Content-Based Retr Clin Decis Support 7723:21–32Google Scholar
  17. 17.
    MutliscaleHuman FP7 partners. [Online].
  18. 18.
  19. 19.
    Politecmed consortium. [Online]. REF-
  20. 20.
  21. 21.
    Gaslini Children’s Hospital. [Online].
  22. 22.
    VTK wiki. [Online].
  23. 23.
    (2011–2013) Apache Jena framework. [Online].
  24. 24.
    ARQ-A SPARQL Processor for Jena. [Online].
  25. 25.
    Agibetov A, Catalano CE, Patane’ G, Spagnuolo M (2015) Ontology modularization for multi-scale biomedical data management and visualization. In: CARS workshop on multiscale digital patient (MDP)Google Scholar
  26. 26.
    Agibetov A, Vaquero RMM, Friese K, Patanè G, Spagnuolo M, Wolter F (2014) Integrated visualization and analysis of a multi-scale biomedical knowledge space. In: Proceedings of the EuroVis workshop on visual analytics, pp 25–29Google Scholar
  27. 27.
    Banerjee I, Laga H, Patané G, Kurtek S, Srivastava A, Spagnuolo M (2015) Generation of 3D canonical anatomical models: an experience on carpal bones. In: New trends in image analysis and processing—ICIAP 2015, Genova, In pressGoogle Scholar
  28. 28.
    Kahana J, Koivunen MR, Prud’Hommeauxb E, Swick RR (2002) Annotea: an open RDF infrastructure for shared Web annotations. Comput Netw 39(5):589–608Google Scholar
  29. 29.
    Hunter J, Cole T, Sanderson R, Van de Sompel H (2010) The open annotation collaboration: a data model to support sharing and interoperability of scholarly annotations. In: Digital humanities, pp. 175–178Google Scholar
  30. 30.
    Chen X, Golovinskiy A, Funkhouser T (2009) A benchmark for 3D mesh segmentation. ACM Trans Gr 28(3):73Google Scholar
  31. 31.
    Østergaard M, PeterfConaghan PC, McQueen F, Bird P, Ejbjerg B, Shnier R, O’Connor P, Klarlund M, Emery P, Genant H, Lassere M, Edmonds J (2003) OMERACT rheumatoid arthritis magnetic resonance imaging studies. Core set of MRI acquisitions, joint pathology definitions, and the OMERACT RA-MRI scoring system. J Rheumatol 30(6):1385–1386Google Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  1. 1.CNR-IMATI GenovaGenovaItaly

Personalised recommendations