Guided Structure-Aligned Segmentation of Volumetric Data

  • Michelle HollowayEmail author
  • Anahita Sanandaji
  • Deniece Yates
  • Amali Krigger
  • Ross Sowell
  • Ruth West
  • Cindy Grimm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9474)


Segmentation of volumetric images is considered a time and resource intensive bottleneck in scientific endeavors. Automatic methods are becoming more reliable, but many data sets still require manual intervention. Key difficulties include navigating the 3D image, determining where to place marks, and maintaining consistency between marks and segmentations. Clinical practice often requires segmenting many different instances of a specific structure. In this research we leverage the similarity of a repeated segmentation task to address these difficulties and reduce the cognitive load for segmenting on non-traditional planes. We propose the idea of guided contouring protocols that provide guidance in the form of an automatic navigation path to arbitrary cross sections, example marks from similar data sets, and text instructions. We present a user study that shows the usability of this system with non-expert users in terms of segmentation accuracy, consistency, and efficiency.



The authors would like to thank Dr. Daniel Low, formerly of the Washington University School of Medicine in St. Louis for the liver data sets, Dr. Sandra Rugonyi of Oregon Health & Science University for the aorta data sets, and Dr. Philip Bayly of Washington University in St. Louis for the ferret brain data sets.

This work was supported by the National Science Foundation under grants DEB-1053554 and IIS-1302142.


  1. 1.
    de Bruin, P., Dercksen, V., Post, F., Vossepoel, A., Streekstra, G.: Interactive 3D segmentation using connected orthogonal contours. Comput. Biol. Med. 35, 329–346 (2005)CrossRefGoogle Scholar
  2. 2.
    Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. Comput. Graph. Forum 17, 167–174 (1998)CrossRefGoogle Scholar
  3. 3.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  4. 4.
    Foppiano, F., Fiorino, C., Frezza, G., Greco, C., Valdagni, R.: The impact of contouring uncertainty on rectal 3D dose-volume data: results of a dummy run in a multicenter trial. Int. J. Radiat. Oncol. Biol. Phys. 57(2), 573–579 (2003)CrossRefGoogle Scholar
  5. 5.
    Hamarneh, G., Yang, J., McIntosh, C., Langille, M.: 3d live-wire-based semi-automatic segmentation of medical images. SPIE Med. Imaging 5747, 1597–1603 (2005)Google Scholar
  6. 6.
    Heckel, F., Konrad, O., Hahn, H.K., Peitgen, H.O.: Interactive 3D medical image segmentation with energy-minimizing implicit functions. Comput. Graph. Spec. Issue Vis. Comput. Biol. Med. 35(2), 275–287 (2011)Google Scholar
  7. 7.
    Khan, A., Mordatch, I., Fitzmaurice, G., Matejka, J., Kurtenbach, G.: Viewcube: a 3d orientation indicator and controller. In: Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games, I3D 2008, pp. 17–25. ACM (2008)Google Scholar
  8. 8.
    Macedo, I., Gois, J., Velho, L.: Hermite interpolation of implicit surfaces with radial basis functions. In: 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI), pp. 1–8, October 2009Google Scholar
  9. 9.
    Prassni, J.S., Ropinski, T., Hinrichs, K.: Uncertainty-aware guided volume segmentation. IEEE Trans. Vis. Comput. Graph. 16(6), 1358–1365 (2010)CrossRefGoogle Scholar
  10. 10.
    Sowell, R., Liu, L., Ju, T., Grimm, C., Abraham, C., Gokhroo, G., Low, D.: VolumeViewer: an interactive tool for fitting surfaces to volume data. In: SBIM 2009: Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling, pp. 141–148. ACM (2009)Google Scholar
  11. 11.
    Sowell, R.T.: Modeling Surfaces from Volume Data Using Nonparallel Contours. Ph.D. thesis, Washington Univ. in St. Louis (2012)Google Scholar
  12. 12.
    Stoakley, R., Conway, M.J., Pausch, R.: Virtual reality on a wim: interactive worlds in miniature. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1995, pp. 265–272. ACM (1995)Google Scholar
  13. 13.
    Top, A., Hamarneh, G., Abugharbieh, R.: Active learning for interactive 3d image segmentation. In: Proceedings of the 14th International Conference on Medical Image Computing and Computer-assisted Intervention - Part III, pp. 603–610 (2011)Google Scholar
  14. 14.
    Top, A., Hamarneh, G., Abugharbieh, R.: Spotlight: automated confidence-based user guidance for increasing efficiency in interactive 3D image segmentation. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 204–213. Springer, Heidelberg (2011) Google Scholar
  15. 15.
    Weiss, E., Richter, S., Krauss, T., Metzelthin, S.I., Hille, A., Pradier, O., Siekmeyer, B., Vorwerk, H., Hess, C.F.: Conformal radiotherapy planning of cervix carcinoma: differences in the delineation of the clinical target volume. A comparison between gynaecologic and radiation oncologists. Radiother Oncol. 67(1), 87–95 (2003)CrossRefGoogle Scholar
  16. 16.
    Wirjadi, O.: Survey of 3D image segmentation methods. Technical report 123, Fraunhofer ITWM (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michelle Holloway
    • 1
    Email author
  • Anahita Sanandaji
    • 2
  • Deniece Yates
    • 2
  • Amali Krigger
    • 5
  • Ross Sowell
    • 4
  • Ruth West
    • 3
  • Cindy Grimm
    • 2
  1. 1.Washington University in St. LouisSt. LouisUSA
  2. 2.Oregon State UniversityCorvallisUSA
  3. 3.University of North TexasDentonUSA
  4. 4.Cornell CollegeVernonUSA
  5. 5.University of the Virgin IslandsCharlotte AmalieUSA

Personalised recommendations