3-D model supported prostate biopsy simulation and evaluation

  • Jianhua Xuan
  • Yue Wang
  • Isabell A. Sesterhenn
  • Judd W. Moul
  • Seong K. Mun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)


The diagnosis of localized prostate cancer is carried out by standard core needle biopsies under guidance of transrectal ultrasound imaging of the prostate gland. This paper describes a 3-D model supported virtual environment for prostate cancer diagnosis and biopsy design. A 3-D deformable reconstruction algorithm is developed to define object surface from digitally-imaged surgical specimens. A virtual environment with a multimodal visualization capability is integrated to simulate prostate biopsy protocols. The new system permits an accurate graphical modeling of the object of interest, the localization and quantification of tumors, and the definition of the pathways of biopsy needles. The technique allows the medical experts to probe and manipulate the data in 3-D view space and to evaluate the performance of simulated biopsies subsequently optimize biopsy techniques. Results and analysis of our experiments demonstrate the effectiveness of the individual modular components of the approach. We conclude with an application of the complete framework to a prostate biopsy simulation and evaluation task.


Prostate Cancer Localize Prostate Cancer Cancer Detection Rate Biopsy Technique Ejaculatory Duct 
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.


  1. 1.
    A. Chiarodo, “National Cancer Institute roundtable on prostate cancer: Future directions,” Cancer Res. vol. 51, pp. 2498–2505, 1991.PubMedGoogle Scholar
  2. 2.
    J. Xuan, I. Sesterhenn, W. Hayes, Y. Wang, T. Adali, M. Freedman, and S. K. Mun, “Surface reconstruction and visualization of surgical prostate models,” Medical Imaging, Newport Beach, Feb. 1997.Google Scholar
  3. 3.
    D. Terzopoulos, A. Witkin, and M. Kass, “Symmetry-seeking models and 3D object reconstruction,” Int. J. Computer Vision, vol. 1, pp. 211–221, 1987.CrossRefGoogle Scholar
  4. 4.
    Zienkiewicz, The Finite Element Method, The Third Edition, McGraw-Hill Book, 1967.Google Scholar
  5. 5.
    G. Celniker and D. Gossard, “Deformable curve and surface finite-elements for freeform shape design,” Computer Graphics, Vol. 25, No. 4, pp. 257–266, July 1991.CrossRefGoogle Scholar
  6. 6.
    T. McInerney, and D. Terzopoulos, “A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis,” Computerized Medical Imaging and Graphics, vol. 19, no. 1, pp. 69–83, 1995.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jianhua Xuan
    • 1
  • Yue Wang
    • 2
    • 5
  • Isabell A. Sesterhenn
    • 3
  • Judd W. Moul
    • 4
  • Seong K. Mun
    • 5
  1. 1.University of Maryland Baltimore CountyBaltimoreUSA
  2. 2.The Catholic University of AmericaWashington, DCUSA
  3. 3.Armed Forces Institute of PathologyWashington, DCUSA
  4. 4.Walter Reed Army Medical CenterWashinton, DCUSA
  5. 5.Georgetwon University Medical CenterWashington, DCUSA

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