Abstract
A novel method of prostate segmentation in a new CT data making use of explicit knowledge about the prostate is proposed. The segmentation procedure is based on active shape statistical model (ASM) of the prostate, calculated using available data base of CTs annotated by medical doctors. In the paper the problem of automatic calculation of corresponding prostate landmarks in different CTs, which are absolutely necessary for the ASM, is solved in a new manner by: 1) finding parameters of affine and B-spline transformations in groupwise registration framework ensuring pixel-based registration of all available CTs in one common co-ordinate system, 2) performing forward affine and B-spline transformation of the annotated prostate contours into this co-ordinate system, 3) averaging them - interpolation & re-sampling, 4) propagation (projection) of mean landmarks, obtained in common co-ordinate system, to the training CTs using the backward transformation. Having the same prostate landmarks in set of CTs, the ASM of the prostate is calculated (its mean shape and tendencies to its direction variations). The result of matching ASM to the data is treated as the prostate segmentation result. Obtained results are presented and discussed in the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cancer Research UK, Prostate cancer - UK incidence statistics, http://info.Cancerresearchuk.org/cancerstats/types/prostate/incidence/
Horwich, V.A., Parker, C., Kataja, V.: Prostate cancer: ESMO Clinical Recommendations for diagnosis, treatment and follow-up. Annals of Oncology 20, iv76–iv78 (2009)
Skalski, A., et al.: Computed Tomography - based radiotherapy planning on the example of prostate cancer: Application of Level-Set segmentation method guided by atlas-type knowledge. In: Conf. ISABEL 2011. ACM Digital Library (2011) ISBN 978-1-4503-0913-4/11/10
Pekar, V., McNutt, T.R., Kaus, M.R.: Automated model-based organ delineation for radiotherapy planning in prostatic region. Int. J. Radiation Oncology Biol. Phys. 60(3), 973–980 (2004)
Acosta, O., Dowling, J., Cazoulat, G., Simon, A., Salvado, O., de Crevoisier, R., Haigron, P.: 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: Madabhushi, A., Dowling, J., Yan, P., Fenster, A., Abolmaesumi, P., Hata, N. (eds.) MICCAI 2010. LNCS, vol. 6367, pp. 42–51. Springer, Heidelberg (2010)
Lu, C., et al.: An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Med. Image Anal. 15, 772–785 (2011)
Chen, S., Lovelock, M., Radke, R.J.: Segmenting the prostate and rectum in CT imagery using anatomical constraints. Med. Image Anal. 15(1), 1–11 (2011)
Klein, S., et al.: Segmentation of the prostate in MR images by Atlas Matching. In: 4th IEEE Int. Symposium on Biomedical Imaging From Nano to Macro, pp. 1300–1303 (2007)
Pasquier, D., et al.: Automatic segmentation on pelvic structures from Magnetic Resonance images for prostate cancer radiotherapy. Int. J. Radiation Oncology Biol. Phys. 68(2), 592–600 (2007)
Martin, S., Daanen, V., Troccaz, J.: Atlas-based prostate segmentation using an hybrid registration. International J. of CARS 3(6), 485–492 (2008)
Dowling, J., Lambert, J., Parker, J., Greer, P.B., Fripp, J., Denham, J., Ourselin, S., Salvado, O.: Automatic MRI Atlas-Based External Beam Radiation Therapy Treatment Planning for Prostate Cancer. In: Madabhushi, A., Dowling, J., Yan, P., Fenster, A., Abolmaesumi, P., Hata, N. (eds.) MICCAI 2010. LNCS, vol. 6367, pp. 25–33. Springer, Heidelberg (2010)
Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: A trainable method of parametric shape description. Image Vision Comput. 10(5), 289–294 (1992)
Cootes, T.F., Taylor, C.J.: Statistical models of appearance for computer vision. Technical Report. University of Manchester (2004)
Gao, X., Li, et al.: A review of Active Appearance Models. IEEE Trans. on Systems, Man, and Cybernetics - Part C: Applications and reviews 40(2), 145–158 (2010)
Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: A review. Med. Image Anal. 13, 543–563 (2009)
Frangi, A.F., Rueckert, D., Schnabel, J.A., Niessen, W.J.: Automatic 3D ASM Construction via Atlas-Based Landmarking and Volumetric Elastic Registration. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 78–91. Springer, Heidelberg (2001)
Frangi, A.F., et al.: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans. Med. Imaging 21(9), 1151–1166 (2002)
Ordas, S., et al.: A statistical shape model of the whole heart and its application to model-based segmentation. In: SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images, vol. 6511. SPIE (2007)
van Assen, H.C., et al.: SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med. Image Anal. 10(2), 286–303 (2006)
Jeong, Y., Radke, R.: Reslicing axially sampled 3D shapes using elliptic Fourier descriptors. Med. Image Anal. 11(2), 197–206 (2007)
Tobon-Gomez, C., et al.: Automatic Construction of 3D-ASM Intensity Models by Simulating Image Acquisition: Application to Myocardial Gated SPECT Studies. IEEE Transactions on Medical Imaging 27(11), 1655–1667 (2008)
Cootes, T.F., Cooper, D., Taylor, C.J., Graham, J.: Active Shape Models - Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Rueckert, D., Sonoda, L.I., Hayes, C., et al.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. on Med. Imag. 18(8), 712–721 (1999)
Balci, S.K., Golland, P., Wells, W.M.: Non-rigid Groupwise Registration using B-Spline Deformation Model. The Insight Journal (2007), doi: http://hdl.handle.net/1926/568
Kroon, D.-J.: Active Shape Model (ASM) and Active Appearance Model (AAM), http://www.mathworks.com/matlabcentral/fileexchange/26706-active-shape-model-asm-and-active-appearance-model-aam
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skalski, A., Kos, A., Zieliński, T. (2012). Using ASM in CT Data Segmentaion for Prostate Radiotherapy. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_73
Download citation
DOI: https://doi.org/10.1007/978-3-642-33564-8_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33563-1
Online ISBN: 978-3-642-33564-8
eBook Packages: Computer ScienceComputer Science (R0)