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Pathology Hinting as the Combination of Automatic Segmentation with a Statistical Shape Model

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7512)


With improvements in acquisition speed and quality, the amount of medical image data to be screened by clinicians is starting to become challenging in the daily clinical practice. To quickly visualize and find abnormalities in medical images, we propose a new method combining segmentation algorithms with statistical shape models. A statistical shape model built from a healthy population will have a close fit in healthy regions. The model will however not fit to morphological abnormalities often present in the areas of pathologies. Using the residual fitting error of the statistical shape model, pathologies can be visualized very quickly. This idea is applied to finding drusen in the retinal pigment epithelium (RPE) of optical coherence tomography (OCT) volumes. A segmentation technique able to accurately segment drusen in patients with age-related macular degeneration (AMD) is applied. The segmentation is then analyzed with a statistical shape model to visualize potentially pathological areas. An extensive evaluation is performed to validate the segmentation algorithm, as well as the quality and sensitivity of the hinting system. Most of the drusen with a height of 85.5 μm were detected, and all drusen at least 93.6 μm high were detected.


  • pathology hinting
  • statistical shape model
  • multi-surface segmentation
  • optical coherence tomography


  1. Garvin, M.K., Abràmoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Transactions on Medical Imaging 28(9), 1436–1447 (2009)

    CrossRef  Google Scholar 

  2. Song, Q., Wu, X., Liu, Y., Garvin, M., Sonka, M.: Simultaneous searching of globally optimal interacting surfaces with shape priors. In: IEEE International Conference on Computer Vision and Pattern Recognition (2010)

    Google Scholar 

  3. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)

    CrossRef  Google Scholar 

  4. Wu, X., Chen, D.Z.: Optimal Net Surface Problems with Applications. In: Widmayer, P., Eidenbenz, S., Triguero, F., Morales, R., Conejo, R., Hennessy, M. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 1029–1042. Springer, Heidelberg (2002)

    CrossRef  Google Scholar 

  5. Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric images - a graph-theoretic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 119–134 (2006)

    CrossRef  Google Scholar 

  6. Ryan, S.J., Wilkinson, C.P.: Retina. Mosby (2005)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Dufour, P.A., Abdillahi, H., Ceklic, L., Wolf-Schnurrbusch, U., Kowal, J. (2012). Pathology Hinting as the Combination of Automatic Segmentation with a Statistical Shape Model. 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 7512. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33453-5

  • Online ISBN: 978-3-642-33454-2

  • eBook Packages: Computer ScienceComputer Science (R0)