Advertisement

A Pattern Recognition Approach to Zonal Segmentation of the Prostate on MRI

  • Geert Litjens
  • Oscar Debats
  • Wendy van de Ven
  • Nico Karssemeijer
  • Henkjan Huisman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)

Abstract

Zonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 ± 0.03 for the central gland and 0.75 ± 0.07 for the peripheral zone, compared to 0.87 ± 0.04 and 0.76 ± 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate.

Keywords

prostate MRI segmentation voxel classification atlas 

References

  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2012. CA Cancer J. Clin. 62, 10–29 (2012)CrossRefGoogle Scholar
  2. 2.
    Kitajima, K., Kaji, Y., Fukabori, Y., Yoshida, K., Suganuma, N., Sugimura, K.: Prostate cancer detection with 3 T MRI: comparison of diffusion-weighted imaging and dynamic contrast-enhanced MRI in combination with T2-weighted imaging. J. Magn. Reson. Imaging 31, 625–631 (2010)CrossRefGoogle Scholar
  3. 3.
    Chan, I., Wells, W., Mulkern, R.V., Haker, S., Zhang, J., Zou, K.H., Maier, S.E., Tempany, C.M.C.: Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Med. Phys. 30, 2390–2398 (2003)CrossRefGoogle Scholar
  4. 4.
    Viswanath, S.E., Bloch, N.B., Chappelow, J.C., Toth, R., Rofsky, N.M., Genega, E.M., Lenkinski, R.E., Madabhushi, A.: Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2-weighted MR imagery. J. Magn. Reson. Imaging (February 2012)Google Scholar
  5. 5.
    Villeirs, G.M., Verstraete, K.L., De Neve, W.J., De Meerleer, G.O.: Magnetic resonance imaging anatomy of the prostate and periprostatic area: a guide for radiotherapists. Radiother. Oncol. 76(1), 99–106 (2005)CrossRefGoogle Scholar
  6. 6.
    Klein, S., van der Heide, U.A., Lips, I.M., van Vulpen, M., Staring, M., Pluim, J.P.W.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 35, 1407–1417 (2008)CrossRefGoogle Scholar
  7. 7.
    Toth, R., Tiwari, P., Rosen, M., Reed, G., Kurhanewicz, J., Kalyanpur, A., Pungavkar, S., Madabhushi, A.: A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation. Med. Image Anal. 15, 214–225 (2011)CrossRefGoogle Scholar
  8. 8.
    Makni, N., Iancu, A., Colot, O., Puech, P., Mordon, S., Betrouni, N.: Zonal segmentation of prostate using multispectral magnetic resonance images. Med. Phys. 38, 6093 (2011)CrossRefGoogle Scholar
  9. 9.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23, 903–921 (2004)CrossRefGoogle Scholar
  10. 10.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010)CrossRefGoogle Scholar
  11. 11.
    Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man. Cybern. 19, 1264–1274 (1989)CrossRefGoogle Scholar
  12. 12.
    Li, H., Giger, M.L., Olopade, O.I., Margolis, A., Lan, L., Chinander, M.R.: Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms. Acad. Radiol. 12, 863–873 (2005)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Geert Litjens
    • 1
  • Oscar Debats
    • 1
  • Wendy van de Ven
    • 1
  • Nico Karssemeijer
    • 1
  • Henkjan Huisman
    • 1
  1. 1.Radboud University Nijmegen Medical CentreNijmegenThe Netherlands

Personalised recommendations