A Survey of Cervix Segmentation Methods in Magnetic Resonance Images

  • Soumya Ghose
  • Lois Holloway
  • Karen Lim
  • Philip Chan
  • Jacqueline Veera
  • Shalini K. Vinod
  • Gary Liney
  • Peter B. Greer
  • Jason Dowling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

Radiotherapy is an effective therapy in the treatment of cervix cancer. However tumor and normal tissue motion and shape deformation of the cervix, the bladder and the rectum over the course of the treatment can limit the efficacy of radiotherapy and safe delivery of the dose. A number of studies have presented the potential benefits of adaptive radiotherapy for cervix cancer with high soft tissue contrast magnetic resonance images. To enable practical implementation of adaptive radiotherapy for the cervix, computer aided segmentation is necessary. Accurate computer aided automatic or semi-automatic segmentation of the cervix is a challenging task due to inter patient shape variation, soft tissue deformation, organ motion, and anatomical changes during the course of the treatment. This article reviews the methods developed for cervix segmentation in magnetic resonance images. The objective of this work is to present different methods for cervix segmentation in the literature highlighting their similarities, differences, strengths and weaknesses.

Keywords

Cervix segmentation methods registration statistical shape models magnetic resonance imaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cervical Cancer Statistics (2013), http://www.cancerresearchuk.org/cancer-info/cancerstats/types/cervical (accessed on June 1, 2013)
  2. 2.
    Berendsen, F.F., van der Heide, U.A., Langerak, T.R., Kotte, A.N., Pluim, J.P.: Free-form image registration regularized by a statistical shape model: application to organ segmentation in cervical MR. Computer Vision and Image Understanding (2013)Google Scholar
  3. 3.
    Bondar, L., Hoogeman, M., Mens, J.W., Dhawtal, G., de Pree, I., Ahmad, R., Quint, S., Heijmen, B.: Towards an individualized target motion management for IMRT of cervical cancer based on model-predicted cervix-uterus shape and position. Radiotherapy and Oncology 99, 240–245 (2011)CrossRefGoogle Scholar
  4. 4.
    Chan, P., Dinniwell, R., Haider, M.A., Cho, Y.B., Jaffray, D., Lockwood, G., Levin, W., Manchul, L., Fyles, A., Milosevic, M.: Inter- and intrafractional tumor and organ movement in patients with cervical cancer undergoing radiotherapy: A cinematic-MRI point-of-interest study. International Journal of Radiation Oncology Biology Physics 70, 1507–1515 (2008)CrossRefGoogle Scholar
  5. 5.
    Chandra, S.S., Dowling, J., Shen, K.K., Raniga, P., Pluim, J.P.W., Greer, P.B., Salvado, O., Fripp, J.: Patient specific prostate segmentation in 3D magnetic resonance images. IEEE Trans. Med. Imaging 31(10), 1955–1964 (2012)CrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Hill, A., Taylor, C.J., Haslam, J.: The Use of Active Shape Model for Locating Structures in Medical Images. Image and Vision Computing 12, 355–366 (1994)CrossRefGoogle Scholar
  7. 7.
    Cremers, D., Osher, S., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision 69(3), 335–351 (2006)CrossRefGoogle Scholar
  8. 8.
    Dowling, J., Lambert, J., Parker, J., Salvado, O., Fripp, J., Wratten, C., Capp, A., Denham, J., Greer, P.: An atlas-based electron density mapping method for magnetic resonance imaging (MRI)-alone treatment planning and adaptive MRI-based prostate radiation therapy. International Journal of Radiation Oncology Biology Physics 83, e5–e11 (2012)Google Scholar
  9. 9.
    Klein, S., van der Heide, U.A., Lipps, I.M., Vulpen, M.V., Staring, M., Pluim, J.P.W.: Automatic Segmentation of the Prostate in 3D MR Images by Atlas Matching Using Localized Mutual Information. Medical Physics 35, 1407–1417 (2008)CrossRefGoogle Scholar
  10. 10.
    Lim, K., Kelly, V., Stewart, J., Xie, J., Cho, Y.B., Moseley, J.B., Brock, K., Fyles, A., Lundin, A., Rehbinder, H., Milosevic, M.: Pelvic radiotherapy for cancer of the cervix: Is what you plan actually what you deliver? International Journal of Radiation Oncology Biology Physics 74, 304–312 (2009)CrossRefGoogle Scholar
  11. 11.
    Lu, C., Chelikani, S., Jaffray, D.A., Milosevic, M.F., Staib, L.H., Duncan, J.S.: Simultaneous nonrigid registration, segmentation, and tumor detection in MRI guided cervical cancer radiation therapy. IEEE Trans. Med. Imaging 31(6), 1213–1227 (2012)CrossRefGoogle Scholar
  12. 12.
    Staring, M., van der Heide, U.A., Klein, S., Viergever, M.A., Pluim, J.P.W.: Registration of cervical MRI using multifeature mutual information. IEEE Trans. Med. Imaging 28(9), 1412–1421 (2009)CrossRefGoogle Scholar
  13. 13.
    Studholme, C., Hill, D.L.J., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 72(1), 71–86 (1999)CrossRefGoogle Scholar
  14. 14.
    Toth, R., Bloch, B.N., Genega, E.M., Rofsky, N.M., Lenkinski, R.E., Rosen, M.A., Kalyanpur, A., Pungavkar, S., Madabhushi, A.: Accurate prostate volume estimation using multifeature active shape models on T2-weighted MR. Academic Radiology 18, 745–754 (2011)CrossRefGoogle Scholar
  15. 15.
    Toth, R., Madabhushi, A.: Multifeature landmark-free active appearance models: Application to prostate MRI segmentation. IEEE Trans. Med. Imaging 31(8), 1638–1650 (2012)CrossRefGoogle Scholar
  16. 16.
    Viswanathan, A., Dimopoulos, J., Kirisits, C., Berger, D., Potter, R.: Computed tomography versus magnetic resonance imaging-based contouring in cervical cancer brachytherapy: results of a prospective trial and preliminary guidelines for standardized contours. International Journal of Radiation Oncology Biology Physics 68, 491–498 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soumya Ghose
    • 1
  • Lois Holloway
    • 2
    • 3
    • 4
  • Karen Lim
    • 2
  • Philip Chan
    • 5
  • Jacqueline Veera
    • 2
  • Shalini K. Vinod
    • 2
    • 9
    • 10
  • Gary Liney
    • 6
  • Peter B. Greer
    • 7
    • 8
  • Jason Dowling
    • 1
  1. 1.CSIRO Computational InformaticsHerstonAustralia
  2. 2.Department of Radiation OncologyLiverpool HospitalLiverpoolAustralia
  3. 3.Institute of Medical PhysicsSydney UniversityDarlingtonAustralia
  4. 4.Centre For Medical Radiation Physics, Northfields Ave, Wollongong NSW 2522University of WollongongAustralia
  5. 5.Royal Brisbane and Women’s HospitalHerstonAustralia
  6. 6.Ingham Institute for Applied Medical ResearchLiverpool HospitalLiverpoolAustralia
  7. 7.Department of Radiation OncologyCalvary Mater Newcastle HospitalWaratahAustralia
  8. 8.Department of PhysicsUniversity of NewcastleCallaghanAustralia
  9. 9.University of Western SydneyRichmondAustralia
  10. 10.South Western Clinical SchoolUniversity of NSWSydneyAustralia

Personalised recommendations