Efficient 3D Multi-region Prostate MRI Segmentation Using Dual Optimization

  • Wu Qiu
  • Jing Yuan
  • Eranga Ukwatta
  • Yue Sun
  • Martin Rajchl
  • Aaron Fenster
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Efficient and accurate extraction of the prostate, in particular its clinically meaningful sub-regions from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, we propose a novel multi-region segmentation approach to simultaneously locating the boundaries of the prostate and its two major sub-regions: the central gland and the peripheral zone. The proposed method utilizes the prior knowledge of the spatial region consistency and employs a customized prostate appearance model to simultaneously segment multiple clinically meaningful regions. We solve the resulted challenging combinatorial optimization problem by means of convex relaxation, for which we introduce a novel spatially continuous flow-maximization model and demonstrate its duality to the investigated convex relaxed optimization problem with the region consistency constraint. Moreover, the proposed continuous max-flow model naturally leads to a new and efficient continuous max-flow based algorithm, which enjoys great advantages in numerics and can be readily implemented on GPUs. Experiments using 15 T2-weighted 3D prostate MR images, by inter- and intra-operator variability, demonstrate the promising performance of the proposed approach.


3D Prostate MRI Zonal Segmentation Convex Optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Siegel, R., Naishadham, D., Jemal, A.: Cancer statistics, 2012. CA: A Cancer Journal for Clinicians 62(1), 10–29 (2012)CrossRefGoogle Scholar
  2. 2.
    Leslie, S., Goh, A., Lewandowski, P.M., Huang, E.Y.H., de Castro Abreu, A.L., Berger, A.K., Ahmadi, H., Jayaratna, I., Shoji, S., Gill, I.S., Ukimura, O.: 2050 contemporary image-guided targeted prostate biopsy better characterizes cancer volume, gleason grade and its 3d location compared to systematic biopsy. The Journal of Urology 187(4, suppl.), e827 (2012)Google Scholar
  3. 3.
    Doyle, S., Feldman, M.D., Tomaszewski, J., Madabhushi, A.: A boosted bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Engineering 59(5), 1205–1218 (2012)CrossRefGoogle Scholar
  4. 4.
    Beyersdorff, D., Winkel, A., Hamm, B., Lenk, S., Loening, S.A., Taupitz, M.: MR imaging-guided prostate biopsy with a closed MR unit at 1.5 T: initial results. Radiology 234(2), 576–581 (2005)CrossRefGoogle Scholar
  5. 5.
    McNeal, J.E.: The zonal anatomy of the prostate. The Prostate 2(1), 35–49 (1981)CrossRefGoogle Scholar
  6. 6.
    Villeirs, G., De Meerleer, G.: Magnetic resonance imaging (mri) anatomy of the prostate and application of mri in radiotherapy planning. European Journal of Radiology 63(3), 361–368 (2007)CrossRefGoogle Scholar
  7. 7.
    Haffner, J., Potiron, E., Bouyé, S., Puech, P., Leroy, X., Lemaitre, L., Villers, A.: Peripheral zone prostate cancers: location and intraprostatic patterns of spread at histopathology. The Prostate 69(3), 276–282 (2009)CrossRefGoogle Scholar
  8. 8.
    Reinsberg, S., Payne, G., Riches, S., Ashley, S., Brewster, J., Morgan, V., et al.: Combined use of diffusion-weighted mri and 1h mr spectroscopy to increase accuracy in prostate cancer detection. American Journal of Roentgenology 188(1), 91–98 (2007)CrossRefGoogle Scholar
  9. 9.
    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. Journal of Magnetic Resonance Imaging 31(3), 625–631 (2010)CrossRefGoogle Scholar
  10. 10.
    Kirby, R., Gilling, P.: Fast facts: benign prostatic hyperplasia. Health Press Limited (2011)Google Scholar
  11. 11.
    Ghose, S., Oliver, A., Martí, R., Lladó, X., Vilanova, J., Freixenet, J., Mitra, J., Sidibé, D., Meriaudeau, F.: A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Computer Methods and Programs in Biomedicine 108(1), 262–287 (2012)CrossRefGoogle Scholar
  12. 12.
    Allen, P., Graham, J., Williamson, D., Hutchinson, C.: Differential segmentation of the prostate in mr images using combined 3d shape modelling and voxel classification. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 410–413. IEEE (2006)Google Scholar
  13. 13.
    Yin, Y., Fotin, S., Periaswamy, S., Kunz, J., Haldankar, H., Muradyan, N., Turkbey, B., Choyke, P.: Fully automated 3d prostate central gland segmentation in mr images: a logismos based approach. In: SPIE, p. 83143B (2012)Google Scholar
  14. 14.
    Makni, N., Iancu, A., Colot, O., Puech, P., Mordon, S., Betrouni, N., et al.: Zonal segmentation of prostate using multispectral magnetic resonance images. Medical Physics 38(11), 6093 (2011)CrossRefGoogle Scholar
  15. 15.
    Litjens, G., Debats, O., van de Ven, W., Karssemeijer, N., Huisman, H.: A pattern recognition approach to zonal segmentation of the prostate on MRI. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 413–420. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Rajchl, M., Yuan, J., White, J.A., Nambakhsh, C.M.S., Ukwatta, E., Li, F., Stirrat, J., Peters, T.M.: A fast convex optimization approach to segmenting 3D scar tissue from delayed-enhancement cardiac MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 659–666. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Delong, A., Gorelick, L., Schmidt, F.R., Veksler, O., Boykov, Y.: Interactive segmentation with super-labels. In: Boykov, Y., Kahl, F., Lempitsky, V., Schmidt, F.R. (eds.) EMMCVPR 2011. LNCS, vol. 6819, pp. 147–162. Springer, Heidelberg (2011)Google Scholar
  18. 18.
    Yuan, J., Qiu, W., Ukwatta, E., Rajchl, M., Sun, Y., Fenster, A.: An efficient convex optimization approach to 3D prostate MRI segmentation with generic star shape prior. In: Prostate MR Image Segmentation Challenge, MICCAI (2012)Google Scholar
  19. 19.
    Yuan, J., Bae, E., Tai, X.-C., Boykov, Y.: A continuous max-flow approach to potts model. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 379–392. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Yuan, J., Bae, E., Tai, X.: A study on continuous max-flow and min-cut approaches. In: CVPR 2010 (2010)Google Scholar
  21. 21.
    Bertsekas, D.P.: Nonlinear Programming. Athena Scientific (September 1999)Google Scholar
  22. 22.
    Hu, N., Downey, D.B., Fenster, A., Ladak, H.M.: Prostate boundary segmentation from 3D ultrasound images. Med. Phys. 30(7), 1648–1659 (2003)CrossRefGoogle Scholar
  23. 23.
    Qiu, W., Yuan, J., Ukwatta, E., Tessier, D., Fenster, A.: Rotational-slice-based prostate segmentation using level set with shape constraint for 3D end-firing TRUS guided biopsy. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 537–544. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Qiu, W., Yuan, J., Ukwatta, E., Tessier, D., Fenster, A.: Prostate segmentation in 3d TURS using convex optimization with shape constraint. In: SPIE, Medical Imaging (2013)Google Scholar
  25. 25.
    Mahdavi, S.S., Moradi, M., Wen, X., Morris, W.J., Salcudean, S.E.: Evaluation of visualization of the prostate gland in vibro-elastography images. Medical Image Analysis 15(4), 589–600 (2011)CrossRefGoogle Scholar
  26. 26.
    Zou, K.H., McDermott, M.P.: Higher-moment approaches to approximate interval estimation for a certain intraclass correlation coefficient. Statistics in Medicine 18(15), 2051–2061 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wu Qiu
    • 1
  • Jing Yuan
    • 1
  • Eranga Ukwatta
    • 1
    • 2
  • Yue Sun
    • 1
    • 2
  • Martin Rajchl
    • 1
    • 2
  • Aaron Fenster
    • 1
    • 2
  1. 1.Robarts Research InstitueWestern UniversityLondonCanada
  2. 2.Department of Biomedical EngineeringWestern UniversityLondonCanada

Personalised recommendations