Automatic prostate segmentation in MR images based on 3D active contours with shape constraints

Planning radiotherapy of prostate cancer requires the prostate segmentation in computed tomography (CT) images that can be manual (done by medical doctors), semi-automatic or automatic. Additional usage of magnetic resonance (MR) images, where the soft tissue are better visible, makes this operation more robust. The paper addresses the problem of prostate segmentation in MR data. Its main contribution relies on novel application of the well-known active contour (AC) method with gradient vector flow (GVF) modification to this task. It is shown in the paper that such approach is successful only after addition of a priori knowledge in the form of prostate shape constraint. The statistical prostate shape was modeled as an ellipse which parameters are calculated exploiting statistical atlas principles. It is presented using Dice similarity measure that the proposed automatic prostate segmentation offers results that are very close to the manual ones and can be used in radiotherapy planning.

[1]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[2]  Siqi Chen,et al.  Segmenting the prostate and rectum in CT imagery using anatomical constraints , 2011, Medical Image Anal..

[3]  Olivier Colot,et al.  Combining a deformable model and a probabilistic framework for an automatic 3D segmentation of prostate on MRI , 2009, International Journal of Computer Assisted Radiology and Surgery.

[4]  G. De Meerleer,et al.  Magnetic resonance imaging (MRI) anatomy of the prostate and application of MRI in radiotherapy planning. , 2007, European journal of radiology.

[5]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[6]  Tomasz P. Zielinski,et al.  Using ASM in CT Data Segmentaion for Prostate Radiotherapy , 2012, ICCVG.

[7]  J. Damilakis,et al.  Image segmentation in treatment planning for prostate cancer using the region growing technique. , 2001, The British journal of radiology.

[8]  Filip Claus,et al.  Interobserver Delineation Variation Using CT versus Combined CT + MRI in Intensity–Modulated Radiotherapy for Prostate Cancer , 2005, Strahlentherapie und Onkologie.

[9]  D P Dearnaley,et al.  Comparison of MRI with CT for the radiotherapy planning of prostate cancer: a feasibility study. , 1999, The British journal of radiology.

[10]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[11]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[12]  Tomasz P. Zielinski,et al.  Computed tomography-based radiotherapy planning on the example of prostate cancer: application of level-set segmentation method guided by atlas-type knowledge , 2011, ISABEL '11.

[13]  Laurent D. Cohen,et al.  Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  A. Horwich,et al.  Prostate cancer: ESMO clinical recommendations for diagnosis, treatment and follow-up. , 2008, Annals of oncology : official journal of the European Society for Medical Oncology.

[15]  Hervé Delingette,et al.  Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models , 2007, MICCAI.

[16]  Reyer Zwiggelaar,et al.  Semi-automatic Segmentation of the Prostate , 2003, IbPRIA.

[17]  Andrzej Skalski,et al.  Comparison of ASM and AAM-based segmentation of prostate image in the CT scans for radiotherapy planning , 2012, 2012 Joint Conference New Trends In Audio & Video And Signal Processing: Algorithms, Architectures, Arrangements And Applications (NTAV/SPA).

[18]  A.S. Dewalle,et al.  3D automatic segmentation and reconstruction of prostate on MR images , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Olivier Salvado,et al.  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 , 2010, Prostate Cancer Imaging.

[20]  G. Thomas,et al.  Semi-Automatic Prostate Segmentation of MR Images Based on Flow Orientation , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.