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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 10–17Cite as

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Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases

Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases

  • Robin Wolz19,
  • Chengwen Chu20,
  • Kazunari Misawa21,
  • Kensaku Mori20,22 &
  • …
  • Daniel Rueckert19 
  • Conference paper
  • 6359 Accesses

  • 36 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7510)

Abstract

A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialised to the segmentation of individual organs or struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal CT scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. This approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. Our results on a dataset of 100 CT scans compare favourable to the state-of-the-art with Dice overlap values of 94%, 91%, 66% and 94% for liver, spleen, pancreas and kidney respectively.

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References

  1. Shimizu, A., Kimoto, T., Kobatake, H., Nawano, S., Shinozaki, K.: Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int. J. CARS 5, 85–98 (2010)

    CrossRef  Google Scholar 

  2. Park, H., Bland, P., Meyer, C.: Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE TMI 22(4), 483–492 (2003)

    Google Scholar 

  3. Okada, T., Yokota, K., Hori, M., Nakamoto, M., Nakamura, H., Sato, Y.: Construction of Hierarchical Multi-Organ Statistical Atlases and Their Application to Multi-Organ Segmentation from CT Images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 502–509. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  4. Rusko, L., Bekes, G., Fidrich, M.: Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images. MedIA 13(6), 871–882 (2009)

    Google Scholar 

  5. Linguraru, M., Pura, J., Chowdhury, A., Summers, R.: Multi-organ Segmentation from Multi-phase Abdominal CT via 4D Graphs Using Enhancement, Shape and Location Optimization. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 89–96. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  6. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)

    CrossRef  Google Scholar 

  7. Artaechevarria, X., Munoz-Barrutia, A., Ortiz-de Solorzano, C.: Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data. IEEE TMI 28(8), 1266–1277 (2009)

    Google Scholar 

  8. Isgum, I., Staring, M., Rutten, A., Prokop, M., Viergever, M., van Ginneken, B.: Multi-Atlas-Based Segmentation With Local Decision Fusion - Application to Cardiac and Aortic Segmentation in CT Scans. IEEE TMI 28(7), 1000–1010 (2009)

    Google Scholar 

  9. Coupe, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)

    CrossRef  Google Scholar 

  10. Warfield, S.K., Zou, K.H., Wells III, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE TMI 23(7), 903–921 (2004)

    Google Scholar 

  11. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE TMI 18(8), 712–721 (1999)

    Google Scholar 

  12. Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)

    CrossRef  Google Scholar 

  13. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23(11), 1222–1239 (2001)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. Imperial College London, London, UK

    Robin Wolz & Daniel Rueckert

  2. Department of Media Science, Nagoya University, Nagoya, Japan

    Chengwen Chu & Kensaku Mori

  3. Aichi Cancer Center, Nagoya, Japan

    Kazunari Misawa

  4. Information and Communications Headquarters, Nagoya University, Japan

    Kensaku Mori

Authors
  1. Robin Wolz
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  2. Chengwen Chu
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  3. Kazunari Misawa
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  4. Kensaku Mori
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  5. Daniel Rueckert
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Editor information

Editors and Affiliations

  1. Inria Sophia Antipolis, Project Team Asclepios, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139,, Cambridge,, MA, USA

    Polina Golland

  3. Information and Communication, Nagoya University, 464-8603, Headquarters, Nagoya, Japan

    Kensaku Mori

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wolz, R., Chu, C., Misawa, K., Mori, K., Rueckert, D. (2012). Multi-organ Abdominal CT Segmentation Using Hierarchically Weighted Subject-Specific Atlases. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33415-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-33415-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33414-6

  • Online ISBN: 978-3-642-33415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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