3D Prostate TRUS Segmentation Using Globally Optimized Volume-Preserving Prior

  • Wu Qiu
  • Martin Rajchl
  • Fumin Guo
  • Yue Sun
  • Eranga Ukwatta
  • Aaron Fenster
  • Jing Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


An efficient and accurate segmentation of 3D transrectal ultrasound (TRUS) images plays an important role in the planning and treatment of the practical 3D TRUS guided prostate biopsy. However, a meaningful segmentation of 3D TRUS images tends to suffer from US speckles, shadowing and missing edges etc, which make it a challenging task to delineate the correct prostate boundaries. In this paper, we propose a novel convex optimization based approach to extracting the prostate surface from the given 3D TRUS image, while preserving a new global volume-size prior. We, especially, study the proposed combinatorial optimization problem by convex relaxation and introduce its dual continuous max-flow formulation with the new bounded flow conservation constraint, which results in an efficient numerical solver implemented on GPUs. Experimental results using 12 patient 3D TRUS images show that the proposed approach while preserving the volume-size prior yielded a mean DSC of 89.5%±2.4%, a MAD of 1.4±0.6 mm, a MAXD of 5.2±3.2 mm, and a VD of 7.5%±6.2% in ~1 minute, deomonstrating the advantages of both accuracy and efficiency. In addition, the low standard deviation of the segmentation accuracy shows a good reliability of the proposed approach.


Image Segmentation 3D Prostate TRUS Image Convex Optimization Volume Preserving Constraint 


  1. 1.
    Jemal, A., Siegel, R., Xu, J., Ward, E.: Cancer statistics, 2010. CA Cancer J. Clin. 60(5), 277–300 (2010)CrossRefGoogle Scholar
  2. 2.
    Qiu, W., Yuchi, M., Ding, M., Tessier, D., Fenster, A.: Needle segmentation using 3D hough transform in 3D TRUS guided prostate transperineal therapy. Med. Phy. 40(4), 042902–1–13 (2013)Google Scholar
  3. 3.
    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.: 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(suppl. 4), e827 (2050)Google Scholar
  4. 4.
    Sonn, G.A., Natarajan, S., Margolis, D.J., MacAiran, M., Lieu, P., Huang, J., Dorey, F.J., Marks, L.S.: Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. The Journal of Urology 189(1), 86–92 (2013)CrossRefGoogle Scholar
  5. 5.
    Qiu, W., Yuan, J., Ukwatta, E., Yue, S., Rajchl, M., Fenster, A.: Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3d trus and mr images. IEEE Trans. Med. Imag. 33(4), 947–960 (2014)CrossRefGoogle Scholar
  6. 6.
    Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al.: Evaluation of prostate segmentation algorithms for mri: the promise12 challenge. Med. Imag. Anal. 18(2), 359–373 (2014)CrossRefGoogle Scholar
  7. 7.
    Qiu, W., Yuan, J., Ukwatta, E., Tessier, D., Fenster, A.: 3D prostate segmentation using level set with shape constraint based on rotational slices for 3D end-firing TRUS guided biopsy. Med. Phy. 40(7), 072903–1–12 (2013)Google Scholar
  8. 8.
    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
  9. 9.
    Tutar, I.B., Pathak, S.D., Gong, L., Cho, P.S., Wallner, K., Kim, Y.: Semiautomatic 3D prostate segmentation from TRUS images using spherical harmonics. IEEE Trans. Med. Imaging 25(12), 1645–1654 (2006)CrossRefGoogle Scholar
  10. 10.
    Mahdavi, S.S., Moradi, M., Wen, X., Morris, W.J., Salcudean, S.E.: Evaluation of visualization of the prostate gland in vibro-elastography images. Med. Imag. Anal. 15(4), 589–600 (2011)CrossRefGoogle Scholar
  11. 11.
    Garnier, C., Bellanger, J.J., Wu, K., Shu, H., Costet, N., Mathieu, R., de Crevoisier, R., Coatrieux, J.L.: Prostate segmentation in HIFU therapy. IEEE Trans. Med. Imag. 30(3), 792–803 (2011)CrossRefGoogle Scholar
  12. 12.
    Akbari, H., Fei, B.: 3D ultrasound image segmentation using wavelet support vector machines. Med. Phys. 39(6), 2972–2984 (2012)CrossRefGoogle Scholar
  13. 13.
    Gorelick, L., Schmidt, F.R., Boykov, Y., Delong, A., Ward, A.: Segmentation with non-linear regional constraints via line-search cuts. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 583–597. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Patt. Anal. Mach. Intel. 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  15. 15.
    Yuan, J., Bae, E., Tai, X.: A study on continuous max-flow and min-cut approaches. In: Davis, L., Malik, J. (eds.) IEEE CVPR, San Francisco, USA, pp. 2217–2224 (2010)Google Scholar
  16. 16.
    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
  17. 17.
    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: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) Prostate MR Image Segmentation Challenge, MICCAI, vol. 7512. Springer (2012)Google Scholar
  18. 18.
    Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: Dual optimization based prostate zonal segmentation in 3D MR images. Med. Imag. Anal. 18(4), 660–673 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wu Qiu
    • 1
  • Martin Rajchl
    • 1
  • Fumin Guo
    • 1
  • Yue Sun
    • 1
  • Eranga Ukwatta
    • 2
  • Aaron Fenster
    • 1
  • Jing Yuan
    • 1
  1. 1.Robarts Research InstituteUniversity of Western OntarioLondonCanada
  2. 2.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUnited States

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