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Overview of the Third Workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS 2012)

  • Henning Müller
  • Hayit Greenspan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7723)

Abstract

The third workshop on Medical Content–based Retrieval for Clinical Decision Support (MCBR–CDS 2012) took place in connection with the MICCAI conference (Medical Image Computing for Computer–Assisted Intervention) in Nice, France on October 1, 2012. This text gives an overview of the invited presentations and scientific papers presented at the workshop. In the description of the papers the comments and discussions at the workshop are taken into account, highlighting the current tendencies and scientific merits. The workshop finished with a panel that discussed the need of clients of image retrieval software and additional important areas such as the importance of high–quality annotated training and test data sets to advance current research. Such big data sets and a framework for researchers to work on them can have an important impact on the field of image–based decision support in the future.

Keywords

medical image analysis medical information retrieval clinical decision support content–based medical image retrieval 

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References

  1. 1.
    Riding the wave: How europe can gain from the rising tide of scientific data. Submission to the European Comission (October 2010), http://cordis.europa.eu/fp7/ict/e-infrastructure/docs/hlg-sdi-report.pdf
  2. 2.
    Akgül, C., Rubin, D., Napel, S., Beaulieu, C., Greenspan, H., Acar, B.: Content–based image retrieval in radiology: Current status and future directions. Journal of Digital Imaging 24(2), 208–222 (2011)CrossRefGoogle Scholar
  3. 3.
    Castellanos, A., Benavent, X., García-Serrano, A., Cigarrán, J.: Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 133–144. Springer, Heidelberg (2013)Google Scholar
  4. 4.
    Catalano, C.E., Robbiano, F., Parascandolo, P., Cesario, L., Vosilla, L., Barbieri, F., Spagnuolo, M., Viano, G., Cimmino, M.A.: Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 21–32. Springer, Heidelberg (2013)Google Scholar
  5. 5.
    El-Kwae, E., Xu, H., Kabuka, M.R.: Content–based retrieval in picture archiving and communication systems. JDI 13(2), 70–81 (2000)Google Scholar
  6. 6.
    Foncubierta–Rodríguez, A., Vargas, A., Platon, A., Poletti, P.–A., Müller, H., Depeursinge, A.: Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 45–55. Springer, Heidelberg (2013)Google Scholar
  7. 7.
    de Herrera, A.G.S., Markonis, D., Müller, H.: Bag–of–Colors for Biomedical Document Image Classification. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 110–121. Springer, Heidelberg (2013)Google Scholar
  8. 8.
    Hersh, W., Müller, H., Kalpathy-Cramer, J., Kim, E., Zhou, X.: The consolidated ImageCLEFmed medical image retrieval task test collection. Journal of Digital Imaging 22(6), 648–655 (2009)CrossRefGoogle Scholar
  9. 9.
    Kalpathy-Cramer, J., Müller, H., Bedrick, S., Eggel, I., García Seco de Herrera, A., Tsikrika, T.: The CLEF 2011 medical image retrieval and classification tasks. In: Working Notes of CLEF 2011 (Cross Language Evaluation Forum) (September 2011)Google Scholar
  10. 10.
    Kato, T.: Database architecture for content–based image retrieval. In: Jamberdino, A.A., Niblack, W. (eds.) Image Storage and Retrieval Systems. SPIE Proc., San Jose, California, vol. 1662, pp. 112–123 (February 1992)Google Scholar
  11. 11.
    Langs, G., Hanbury, A., Menze, B., Müller, H.: VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 92–98. Springer, Heidelberg (2013)Google Scholar
  12. 12.
    Lowe, H.J., Antipov, I., Hersh, W., Smith, C.A.: Towards knowledge–based retrieval of medical images. The role of semantic indexing, image content representation and knowledge–based retrieval. In: Proceedings of the Annual Symposium of the American Society for Medical Informatics (AMIA), Nashville, TN, USA, pp. 882–886 (October 1998)Google Scholar
  13. 13.
    Müller, H., Clough, P., Hersh, W., Deselaers, T., Lehmann, T., Geissbuhler, A.: Evaluation axes for medical image retrieval systems: the ImageCLEF experience. In: MULTIMEDIA 2005: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 1014–1022. ACM Press, New York (2005)CrossRefGoogle Scholar
  14. 14.
    Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content–based image retrieval systems in medicine–clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2004)CrossRefGoogle Scholar
  15. 15.
    Qian, Y., Wang, L., Wang, C., Gao, X.: The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 68–79. Springer, Heidelberg (2013)Google Scholar
  16. 16.
    Quatrehomme, A., Millet, I., Hoa, D., Subsol, G., Puech, W.: Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 80–91. Springer, Heidelberg (2013)Google Scholar
  17. 17.
    Quellec, G., Lamard, M., Droueche, Z., Cochener, B., Roux, C., Cazuguel, G.: A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 10–20. Springer, Heidelberg (2013)Google Scholar
  18. 18.
    Simonyan, K., Modat, M., Ourselin, S., Cash, D., Criminisi, A., Zisserman, A.: Immediate ROI Search for 3-D Medical Images. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 56–67. Springer, Heidelberg (2013)Google Scholar
  19. 19.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content–based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  20. 20.
    Stathopoulos, S., Kalamboukis, T.: An SVD–Bypass Latent Semantic Analysis for Image Retrieval. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 122–132. Springer, Heidelberg (2013)Google Scholar
  21. 21.
    Tagare, H.D., Jaffe, C., Duncan, J.: Medical image databases: A content–based retrieval approach. Journal of the American Medical Informatics Association 4(3), 184–198 (1997)CrossRefGoogle Scholar
  22. 22.
    Hegenbart, S., Maimone, S., Uhl, A., Vécsei, A., Wimmer, G.: Customised Frequency Pre-Filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 99–109. Springer, Heidelberg (2013)Google Scholar
  23. 23.
    Yang, S., Shapiro, L., Cunningham, M., Speltz, M., Birgfeld, C., Atmosukarto, I., Lee, S.-I.: Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds.) MCBR-CDS 2012. LNCS, vol. 7723, pp. 33–44. Springer, Heidelberg (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Henning Müller
    • 1
    • 2
  • Hayit Greenspan
    • 3
  1. 1.University of Applied Sciences Western Switzerland (HES–SO)Switzerland
  2. 2.University Hospitals and University of GenevaSwitzerland
  3. 3.University of Tel AvivIsrael

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