Interactive Prostate Segmentation Based on Adaptive Feature Selection and Manifold Regularization

  • Sang Hyun Park
  • Yaozong Gao
  • Yinghuan Shi
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8679)


In this paper, we propose a new learning-based interactive editing method for prostate segmentation. Although many automatic methods have been proposed to segment the prostate, laborious manual correction is still required for many clinical applications due to the limited performance of automatic segmentation. The proposed method is able to flexibly correct wrong parts of the segmentation within a short time, even few scribbles or dots are provided. In order to obtain the robust correction with a few interactions, the discriminative features that can represent mid-level cues beyond image intensity or gradient are adaptively extracted from a local region of interest according to both the training set and the interaction. Then, the labeling problem is formulated as a semi-supervised learning task, which is aimed to preserve the manifold configuration between the labeled and unlabeled voxels. The proposed method is evaluated on a challenging prostate CT image data set with large shape and appearance variations. The automatic segmentation results originally with the average Dice of 0.766 were improved to the average Dice 0.866 after conducting totally 22 interactions for the 12 test images by using our proposed method.


Interactive segmentation prostate feature selection semi-supervised learning manifold regularization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Davis, B.C., Foskey, M., Rosenman, J., Goyal, L., Chang, S., Joshi, S.: Automatic segmentation of intra-treatment CT images for adaptive radiation therapy of the prostate. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 442–450. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Chen, S., Lovelock, D.M., Radke, R.J.: Segmenting the prostate and rectum in ct imagery using anatomical constraints. Medical Image Analysis 15(1), 1–11 (2011)CrossRefzbMATHGoogle Scholar
  3. 3.
    Li, W., Liao, S., Feng, Q., Chen, W., Shen, D.: Learning image context for segmentation of prostate in CT-guided radiotherapy. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 570–578. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on PAMI 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  5. 5.
    Grady, L.: Random walks for image segmentation. IEEE Transactions on PAMI 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  6. 6.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: A randomized correspondence algorithm for structural image editing. In: Proceedings of SIGGRAPH (2009)Google Scholar
  7. 7.
    Park, S.H., Yun, I.D., Lee, S.U.: Data-Driven Interactive 3D Medical Image Segmentation Based on Structured Patch Model. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 196–207. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Anonymous: Learning distance transform for boundary detection and deformable segmentation in ct prostate images. To be submitted (2014)Google Scholar
  9. 9.
    Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Medical Image Analysis 12(6), 731–741 (2008)CrossRefGoogle Scholar
  10. 10.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7, 2399–2434 (2006)zbMATHMathSciNetGoogle Scholar
  11. 11.
    Criminisi, A., Shotton, J., Robertson, D., Konukoglu, E.: Regression forests for efficient anatomy detection and localization in CT studies. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 106–117. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Gao, Y., Liao, S., Shen, D.: Prostate segmentation by sparse representation based classification. Medical Physics 39(10), 6372–6387 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sang Hyun Park
    • 1
  • Yaozong Gao
    • 1
  • Yinghuan Shi
    • 2
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology, BRICUniversity of North Carolina at Chapel HillUSA
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityChina

Personalised recommendations