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Computed tomography-based radiotherapy planning on the example of prostate cancer: application of level-set segmentation method guided by atlas-type knowledge

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Published:26 October 2011Publication History

ABSTRACT

In this invited paper an overview of the Computed Tomography-based cancer radiotherapy planning is given. All planning steps are described with details, i.e. its goals, existing solutions and typical realizations. On this background, as an example, a complete procedure of prostate cancer radiotherapy planning is presented in which application of Level-Set segmentation method guided by a priori atlas-type knowledge is proposed. Developed procedure was verified on data base consisting of 266 CT slices of 4 patients.

References

  1. Cancer Research UK, Prostate cancer - UK incidence statistics. http://info.cancerresearchuk.org/cancerstats/types/prostate/incidence/.Google ScholarGoogle Scholar
  2. Horwich VA., Parker C., Kataja V., 2009. Prostate cancer: ESMO Clinical Recommendations for diagnosis, treatment and follow-up. Annals of Oncology 20 (Supplement 4): iv76--iv78, DOI=http://annonc.oxfordjournals.org/content/20/suppl_4/iv76.full.pdf+html.Google ScholarGoogle Scholar
  3. Huang, J., Kestin L. L., Ye, H., et al., 2011. Analysis of second malignancies after modern radiotherapy versus prostatectomy for localized prostate cancer. Radiotherapy & Oncology, 98, 1 (Jan. 2011), 81--86. DOI=10.1016/j.radonc.2010.09.012Google ScholarGoogle ScholarCross RefCross Ref
  4. MICCAI, Medical Image Computing and Computer Assisted Intervention Society, http://www.miccai.org/focus.Google ScholarGoogle Scholar
  5. Freedman, D., et al., 2005. Model-based segmentation of medical imagery by matching distributions. IEEE Tran. Med. Imag., 24, 3, (Mar. 2005) 281--292.Google ScholarGoogle Scholar
  6. Jeong, Y., Radke, R., 2006. Modeling inter- and intra-patient anatomical variation using a bilinear model. In IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis. (17--22 June 2006). Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Acosta, O., et al., 2010. Atlas Based Segmentation and Mapping of Organs at Risk from Planning CT for the Development of Voxel-Wise Predictive Models of Toxicity in Prostate Radiotherapy. In: A. Madabhushi et al. (Eds.): Prostate Cancer Imaging, LNCS 6367, Springer-Verlag, 42--51. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chen, S., Lovelock, M., and Radke, R. J., 2011. Segmenting the prostate and rectum in CT imagery using anatomical constraints. Med. Imag. Anal. 15, 1, (Feb. 2011), 1--11.Google ScholarGoogle Scholar
  9. Malsch, U., Thieke, C., and Bendl, R., 2006. Fast elastic registration for adaptive radiotherapy. Medical Image Computing and Computer-Assisted Intervention, LNCS 4191 (Copenhagen, Denmark, 1--6 Oct. 2006), 612--619. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Martin, S., Daanen, V., and Troccaz, J., 2008. Atlas-based prostate segmentation using an hybrid registration. International Journal of Computer Assisted Radiology and Surgery, 3, 6, (Dec. 2008), 485--492,Google ScholarGoogle ScholarCross RefCross Ref
  11. Klein, S., et al., 2007. Segmentation of the prostate in MR images by Atlas Matching. In 4th IEEE International Symposium on Biomedical Imaging From Nano to Macro. (Arlington, VA, 12--15 Apr. 2007) 1300--1303.Google ScholarGoogle ScholarCross RefCross Ref
  12. Pasquier D., et al., 2007. Automatic segmentation on pelvic structures from Magnetic Resonance images for prostate cancer radiotherapy. Int. J. Radiation Oncology Biol. Phys., 68, 2, (Feb. 2007) 592--600.Google ScholarGoogle ScholarCross RefCross Ref
  13. Dowling, J., et al., 2010. Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer. In: A. Madabhushi et al. (Eds.): Prostate Cancer Imaging, LNCS 6367, Springer-Verlag, 25--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Balci, S. K., Golland, P., Wells, W. M., 2007. Non-rigid Groupwise Registration using B-Spline Deformation Model. The Insight Journal. (Jul. 2007). DOI=http://hdl.handle.net/1926/568Google ScholarGoogle Scholar
  15. Rueckert, D., Sonoda, L. I., Hayes, C., et al., 1999. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Transactions on Medical Imaging. 18, 8 (Aug. 1999), 712--721.Google ScholarGoogle ScholarCross RefCross Ref
  16. Balci, S. K., Golland, P., Wells, W. M., 2007. Non-rigid Groupwise Registration using B-Spline Deformation Model. The Insight Journal. (Jul. 2007). DOI=http://hdl.handle.net/1926/568Google ScholarGoogle Scholar
  17. Osher, S. and Sethian, A. J., 1988. Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi Formulations. Journal of Computational Physics, 79, (1988), 12--49. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Chan, T. and Vese, L., 1999. An active contour model without edges. Scale-Space Theories in Computer Vision. 141--151. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Osher, S. and Paragios N., 2003. Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer-Verlag, New York. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Mosaliganti, K., et al., 2009. Level set segmentation using coupled active surfaces. The Insight Journal. (Jan. 2009). DOI=http://hdl.handle.net/1926/1533.Google ScholarGoogle Scholar
  21. www.itk.orgGoogle ScholarGoogle Scholar
  22. www.mathworks.comGoogle ScholarGoogle Scholar

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      • Published in

        cover image ACM Other conferences
        ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
        October 2011
        949 pages
        ISBN:9781450309134
        DOI:10.1145/2093698

        Copyright © 2011 ACM

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        Publication History

        • Published: 26 October 2011

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