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Facilitating 3D Spectroscopic Imaging through Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized via Boosted Classifiers

  • Parmeshwar Khurd
  • Leo Grady
  • Kalpitkumar Gajera
  • Mamadou Diallo
  • Peter Gall
  • Martin Requardt
  • Berthold Kiefer
  • Clifford Weiss
  • Ali Kamen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6963)

Abstract

Magnetic resonance imaging (MRI) plays a key role in the diagnosis, staging and treatment monitoring for prostate cancer. Automatic prostate localization in T2-weighted MR images could facilitate labor-intensive cancer imaging techniques such as 3D chemical shift MR spectroscopic imaging as well as advanced analysis techniques for diagnosis and treatment monitoring. We present a novel method for automatic segmentation of the prostate gland in MR images. Accurate prostate segmentation in MR imagery poses unique challenges. These include large variations in prostate anatomy and disease, intensity inhomogeneities, and near-field artifacts induced by endorectal coils. Our system meets these challenges with two key components. First is the automatic center detection of the prostate with a boosted classifier trained on intensity-based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. The second is the use of a shape model in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW. Our system has been validated on a large database of non-isotropic T2-TSE (Turbo Spin Echo) and isotropic T2-SPACE (Sampling Perfection with Application Optimized Contrasts) images.

Keywords

Prostate Gland Peripheral Zone Automatic Segmentation Active Shape Model Intensity Inhomogeneity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Betrouni, N., Puech, P., Dewalle, A., Lopes, R., Dubois, P., Vermandel, M.: 3D automatic segmentation and reconstruction of prostate on MR images. In: IEEE EMBS Conf. (2007)Google Scholar
  2. 2.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  3. 3.
    Dobkin, D., Kirkpatrick, D.: Determining the separation of preprocessed polyhedra - a unified approach. Automata, Languages and Programming 443, 400–413 (1990)zbMATHGoogle Scholar
  4. 4.
    Flores-Tapia, D., Thomas, G., Venugopal, N., McCurdy, B., Pistorius, S.: Semi-automatic MRI prostate segmentation based on wavelet multiscale products. In: IEEE EMBS Conf. (2008)Google Scholar
  5. 5.
    Franiel, T., Ludemann, L., Rudolph, B., Rehbein, H., Stephan, C., Taupitz, M., Beyersdorff, D.: Prostate MR imaging: Tissue characterization with pharmacokinetic volume and blood flow parameters and correlation with histologic parameters. Radiology 252(1), 101–108 (2009)CrossRefGoogle Scholar
  6. 6.
    van Ginneken, B., Heimann, T., Styner, M.: 3D segmentation in the clinic: A grand challenge. In: MICCAI Wshp. 3D Segmentation in the Clinic (2007)Google Scholar
  7. 7.
    Gong, L., Pathak, S., Haynor, D., Cho, P., Kim, Y.: Parametric shape modeling using deformable superellipses for prostate segmentation. TMI 23(3) (2004)Google Scholar
  8. 8.
    Grady, L.: Random walks for image segmentation. IEEE Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  9. 9.
    Klein, S., van der Heide, U.A., Lips, I., van Vulpen, M., Maes, F., Staring, M., Pluim, J.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics 35(4), 1407–1417 (2008)CrossRefGoogle Scholar
  10. 10.
    Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. TMI 18(10), 885–896 (2003)Google Scholar
  11. 11.
    Liu, X., Langer, D.L., Haider, M.A., der Kwast, T.H.V., Evans, A.J., Wernick, M.N., Yetik, I.S.: Unsupervised segmentation of the prostate using MR images based on level set with a shape prior. In: IEEE EMBS Conf. (2009)Google Scholar
  12. 12.
    Rousson, M., Khamene, A., Diallo, M.H., Celi, J.C., Sauer, F.: Constrained surface evolutions for prostate and bladder segmentation in CT images. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 251–260. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Samiee, M., Thomas, G., Fazel-Rezai, R.: Semi-automatic prostate segmentation of MR images based on flow orientation. In: IEEE International Symposium on Signal Processing and Information Technology (2006)Google Scholar
  14. 14.
    Scheenen, T., Heijmink, S., Roell, S., de Kaa, C.H., Knipscheer, B., Witjes, J., Barentsz, J., Heerschap, A.: Three-dimensional proton MR spectroscopy of human prostate at 3 T without endorectal coil. Radiology 245(2), 507–516 (2007)CrossRefGoogle Scholar
  15. 15.
    Toth, R., Chappelow, J., Rosen, M.A., Pungavkar, S., Kalyanpur, A., Madabhushi, A.: Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA). In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 653–661. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    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. Medical Image Analysis 15, 214–225 (2011)CrossRefGoogle Scholar
  17. 17.
    Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W., Willsky, A.: A shape-based approach to the segmentation of medical imagery using level sets. TMI 22(2), 137–154 (2003)Google Scholar
  18. 18.
    Tu, Z., Zhou, X., Barbu, A., Bogoni, L., Comaniciu, D.: Probabilistic 3D polyp detection in CT images: The role of sample alignment. In: CVPR (2006)Google Scholar
  19. 19.
    Turkbey, B., Pinto, P., Choyke, P.L.: Imaging techniques for prostate cancer: implications for focal therapy. Nature Reviews: Urology 6, 191–203 (2009)Google Scholar
  20. 20.
    Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comp. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Parmeshwar Khurd
    • 1
  • Leo Grady
    • 1
  • Kalpitkumar Gajera
    • 1
  • Mamadou Diallo
    • 1
  • Peter Gall
    • 2
  • Martin Requardt
    • 2
  • Berthold Kiefer
    • 2
  • Clifford Weiss
    • 3
  • Ali Kamen
    • 1
  1. 1.Siemens CorporationCorporate ResearchPrincetonUSA
  2. 2.Siemens HealthcareMR OncologyErlangenGermany
  3. 3.Johns Hopkins Medical InstitutionsBaltimoreUSA

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