The Visual Computer

, Volume 26, Issue 6–8, pp 639–648 | Cite as

Collaborative telemedicine for interactive multiuser segmentation of volumetric medical images

  • Seunghyun HanEmail author
  • Niels A. Nijdam
  • Jérôme Schmid
  • Jinman Kim
  • Nadia Magnenat-Thalmann
Original Article


Telemedicine has evolved rapidly in recent years to enable unprecedented access to digital medical data, such as with networked image distribution/sharing and online (distant) collaborative diagnosis, largely due to the advances in telecommunication and multimedia technologies. However, interactive collaboration systems which control editing of an object among multiple users are often limited to a simple “locking” mechanism based on a conventional client/server architecture, where only one user edits the object which is located in a specific server, while all other users become viewers. Such systems fail to provide the needs of a modern day telemedicine applications that demand simultaneous editing of the medical data distributed in diverse local sites. In this study, we introduce a novel system for telemedicine applications, with its application to an interactive segmentation of volumetric medical images. We innovate by proposing a collaborative mechanism with a scalable data sharing architecture which makes users interactively edit on a single shared image scattered in local sites, thus enabling collaborative editing for, e.g., collaborative diagnosis, teaching, and training. We demonstrate our collaborative telemedicine mechanism with a prototype image editing system developed and evaluated with a user case study. Our result suggests that the ability for collaborative editing in a telemedicine context can be of great benefit and hold promising potential for further research.


Telemedicine Teleradiology Multi-user segmentation 


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  1. 1.
    Constantinescu, L., Kim, J., Chan, C., Feng, D.: Automatic mobile device synchronization and remote control system for high-performance medical applications. In: IEEE Proc. Engineering in Medicine and Biology Society (EMBS), pp. 2799–2802 (2007) Google Scholar
  2. 2.
    Costa, M.J., Delingette, H., Novellas, S., Ayache, N.: Automatic segmentation of bladder and prostate using coupled 3D deformable models. In: Int. Conf. on medical image computing and computer assisted intervention (MICCAI), pp. 252–260 (2007) Google Scholar
  3. 3.
    Delaney, D., Ward, T., McLoone, S.: On consistency and network latency in distributed interactive applications – a survey. Presence, Part I 15(4), 465–482 (2006) CrossRefGoogle Scholar
  4. 4.
    Delingette, H.: General object reconstruction based on simplex meshes. Int. J. Comput. Vis. 32, 111–146 (1999) CrossRefGoogle Scholar
  5. 5.
    Dollimore, J., Kindberg, T., Coulouris, G.: Distributed Systems: Concepts and Design. Addison-Wesley, Reading (2005) Google Scholar
  6. 6.
    Eugster, P., Felber, P., Guerraoui, R., Kermarrec, A.: The many faces of publish/subscribe. ACM Comput. Surv. 35(2), 114–131 (2003) CrossRefGoogle Scholar
  7. 7.
    Gilles, B., Moccozet, L., Magnenat-Thalmann, N.: Anatomical modelling of the musculoskeletal system from MRI. In: Larsen, R., Nielsen, M., Sporring, J. (eds) MICCAI 2006, LNCS, pp. 289–296 (2006) Google Scholar
  8. 8.
    Han, S., Lee, D., Ko, I.: A deputy object based presentation semantics split application model for synchronous collaboration in ubiquitous computing environments. In: Proceedings of the Third International Conference on Collaboration Technologies, July (2007) Google Scholar
  9. 9.
    Heimann, T., Munzing, S., Meinzer, H., Wolf, I.: A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation. In: N. Karssemeijer, B. Lelieveldt (eds) Proc. Int. Conf. Information Processing in Medical Imaging (IPMI), pp. 1–12 (2007) Google Scholar
  10. 10.
    Heimann, T., Meinzer, H.: Statistical shape models for 3D medical image segmentation: A review. Med. Image Anal. 13, 543–563 (2009) CrossRefGoogle Scholar
  11. 11.
    Ibanez, L., Schroeder, W., Ng, L., Cates, J., et al.: The ITK software guide. Kitware (2003) Google Scholar
  12. 12.
    Kainmuller, D., Lamecker, H., Zachow, S., Hege, H.-C.: An articulated statistical shape model for accurate hip joint segmentation. In: Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), pp. 6345–6351 (2009) Google Scholar
  13. 13.
    Lee, D., Lim, M., Han, S., Lee, K.: ATLAS: A scalable network framework for distributed virtual environments. Presence 16(2), 125–156 (2007) CrossRefGoogle Scholar
  14. 14.
    Lewis, J.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int. J. Hum.–Comput. Interact. 7(1), 57–78 (1995) CrossRefGoogle Scholar
  15. 15.
    Marescaux, J., Leroy, J., Gagner, M., Rubino, F., Mutter, D., Vix, M., Butner, S.E., Smith, M.K.: Transatlantic robot-assisted telesurgery. Nature 413, 379–380 (2001) CrossRefGoogle Scholar
  16. 16.
    Morillo, P., Orduna, J.M., Fernandez, M., Duato, J.: Improving the performance of distributed virtual environment systems. IEEE Trans. Parallel Distrib. Syst. 16(7), 337–649 (2005) CrossRefGoogle Scholar
  17. 17.
    Olabarriaga, S., Smeulders, A.: Interaction in the segmentation of medical images: A survey. Med. Image Anal. 5, 127–142 (2001) CrossRefGoogle Scholar
  18. 18.
    Park, S., Kim, W., Ihm, I.: Mobile collaborative medical display system. Comput. Methods Programs Biomed. 89(3), 248–260 (2008) CrossRefGoogle Scholar
  19. 19.
    Rialle, V., Lamy, J.B., Noury, N., Bajolle, L.: Telemonitoring of patients at home: a software agent approach. Comput. Methods Programs Biomed. 72(3), 257–268 (2003) CrossRefGoogle Scholar
  20. 20.
    Schmid, J., Magnenat-Thalmann, N.: MRI bone segmentation using deformable models and shape priors. In: Metaxas, D., Axel, L., Szekely, G., Fichtinger, G. (eds) MICCAI 2008, Part I. LNCS, pp. 119–126 (2008) Google Scholar
  21. 21.
    Schmid, J., Nijdam, N., Han, S., Kim, J., Magnenat-Thalmann, N.: Interactive segmentation of volumetric medical images for collaborative telemedicine. In: Modelling the Physiological Human, Proc. 3D Physiological Human Workshop 5903, pp. 13–24 (2009) Google Scholar
  22. 22.
    Simmross-Wattenberg, F., Carranza-Herrezuelo, N., Palacios-Camarero, C., Casaseca-de-la-Higuera, P., Martín-Fernández, M., Aja-Fernández, S., Ruiz-Alzola, J., Westin, C., Alberola-López, C.: Group-Slicer: A collaborative extension of 3D-Slicer. J. Biomed. Informatics 38(6), 431–442 (2005) CrossRefGoogle Scholar
  23. 23.
    Singhal, S., Zida, M.: Networked Virtual Environments: Design and Implementation. Addison-Wesley, Reading (1999) Google Scholar
  24. 24.
    Snel, J., Venema, H., Grimbergen, C.: Deformable triangular surfaces using fast 1-D radial Lagrangian dynamics-segmentation of 3-D MR and CT images of the wrist. IEEE Trans. Med. Imaging 21, 888–903 (2002) CrossRefGoogle Scholar
  25. 25.
    Volino, P., Magnenat-Thalmann, N.: Implementing fast cloth simulation with collision response. Comput. Graph. Int. 2000, 257–266 (2000) Google Scholar
  26. 26.
    Wootton, R., Craig, J., Patterson, V.: Introduction to Telemedicine, 2nd edn. The Royal Society of Medicine Press Ltd, London (2006) Google Scholar
  27. 27.
    Zhang, J., Stahl, J.N., Huang, H.K., Zhou, X., Lou, S.L., Song, K.S.: Real-time teleconsultation with high-resolution and large-volume medical images for collaborative healthcare. IEEE Trans. Inf. Tech. Biomed. 4(2), 178–185 (2000) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Seunghyun Han
    • 1
    Email author
  • Niels A. Nijdam
    • 1
  • Jérôme Schmid
    • 1
  • Jinman Kim
    • 1
  • Nadia Magnenat-Thalmann
    • 1
  1. 1.MIRALabUniversity of GenevaGenevaSwitzerland

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