Advertisement

Geodesic Patch-Based Segmentation

  • Zehan Wang
  • Kanwal K. Bhatia
  • Ben Glocker
  • Antonio Marvao
  • Tim Dawes
  • Kazunari Misawa
  • Kensaku Mori
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.

Keywords

Right Ventricle Geodesic Distance Spatial Context Registration Error Label Propagation 
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.

References

  1. 1.
    Aljabar, P., Heckemann, R.A., Hammers, A., Hajnal, J.V., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. IEEE PAMI 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  3. 3.
    Coupé, P., Manjón, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)CrossRefGoogle Scholar
  4. 4.
    Criminisi, A., Sharp, T., Blake, A.: GeoS: Geodesic Image Segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 99–112. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Eskildsen, S.F., Coupé, P., Fonov, V., Manjón, J.V., Leung, K.K., Guizard, N., Wassef, S.N., Østergaard, L.R., Collins, D.L.: BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59(3), 2362–2373 (2012)CrossRefGoogle Scholar
  6. 6.
    Glocker, B., Pauly, O., Konukoglu, E., Criminisi, A.: Joint Classification-Regression Forests for Spatially Structured Multi-object Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 870–881. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)CrossRefGoogle Scholar
  8. 8.
    Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A.: Neighbourhood approximation using randomized forests. Medical Image Analysis 17(7), 790–804 (2013)CrossRefGoogle Scholar
  9. 9.
    Kumar, N., Zhang, L., Nayar, S.K.: What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 364–378. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.R.: Evaluation of Atlas Selection Strategies for Atlas-Based Image Segmentation with Application to Confocal Microscopy Images of Bee Brains. NeuroImage 21(4), 1428–1442 (2004)CrossRefGoogle Scholar
  11. 11.
    Rousseau, F., Habas, P., Studholme, C.: A Supervised Patch-Based Approach for Human Brain Labeling. IEEE TMI 30(10), 1852–1862 (2011)Google Scholar
  12. 12.
    Toivanen, P.J.: New geodosic distance transforms for gray-scale images. Pattern Recognition Letters 17(5), 437–450 (1996)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Donoghue, C., Rueckert, D.: Patch-based segmentation without registration: Application to knee MRI. In: Wu, G., Zhang, D., Shen, D., Yan, P., Suzuki, K., Wang, F. (eds.) MLMI 2013. LNCS, vol. 8184, pp. 98–105. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Wang, Z., Wolz, R., Tong, T., Rueckert, D.: Spatially Aware Patch-Based Segmentation (SAPS): An Alternative Patch-Based Segmentation Framework. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 93–103. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Wolz, R., Chu, C., Misawa, K., Fujiwara, M., Mori, K., Rueckert, D.: Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE TMI 32(9), 1723–1730 (2013)Google Scholar
  16. 16.
    Zikic, D., Glocker, B., Criminisi, A.: Atlas Encoding by Randomized Forests for Efficient Label Propagation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 66–73. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zehan Wang
    • 1
  • Kanwal K. Bhatia
    • 1
  • Ben Glocker
    • 1
  • Antonio Marvao
    • 2
  • Tim Dawes
    • 2
  • Kazunari Misawa
    • 3
  • Kensaku Mori
    • 4
    • 5
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis Group, Department of ComputingImperial College LondonLondonUK
  2. 2.Institute of Clinical SciencesImperial College LondonLondonUK
  3. 3.Aichi Cancer CenterNagoyaJapan
  4. 4.Department of Media ScienceNagoya UniversityNagoyaJapan
  5. 5.Information and Communications HeadquartersNagoya UniversityNagoyaJapan

Personalised recommendations