Segmenting the prostate and rectum in CT imagery using anatomical constraints

Medical Image Analysis

Volumn 15, Issue 1, 2011, Pages 1-11

  Chen, Siqi ^a   Lovelock D Michael, D M ^b   Radke Richard J , R J ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

Author keywords

Deformable segmentation; Image guided radiation therapy; Prostate and rectum; Segmentation; Shape and appearance model

Indexed keywords

DEFORMABLE SEGMENTATION; IMAGE-GUIDED RADIATION THERAPY; PROSTATE AND RECTUM; SEGMENTATION; SHAPE AND APPEARANCE MODEL;

COMPUTERIZED TOMOGRAPHY; DEFORMATION; RADIOTHERAPY; THREE DIMENSIONAL;

IMAGE SEGMENTATION;

ARTICLE; CANCER RADIOTHERAPY; CLINICAL ARTICLE; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGERY; INTENSITY MODULATED RADIATION THERAPY; MALE; MODEL; PELVIS; PRIORITY JOURNAL; PROSTATE; PROSTATE CANCER; RADIATION DOSE; RECTUM; THREE DIMENSIONAL IMAGING; TREATMENT PLANNING;

ALGORITHMS; BAYES THEOREM; HUMANS; IMAGING, THREE-DIMENSIONAL; MALE; MODELS, ANATOMIC; PROSTATE; PROSTATIC NEOPLASMS; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOTHERAPY, INTENSITY-MODULATED; RECTUM; TOMOGRAPHY, X-RAY COMPUTED;

EID: 78449235459     PISSN: 13618415     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.media.2010.06.004     Document Type: Article

References (49)

1. Bansal, R., Staib, L.H., Chen, Z., Rangarajan, A., et al., 2003. Entropy based dual portal to 3D CT registration incorporating pixel correlation. IEEE Transactions on Medical Imaging 22 (1).
2. Brejl M., Sonka M. Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples. IEEE Transactions on Medical Imaging 2000, 19(10):973-985.
3. Broadhurst, R., Stough, J., Pizer, S., Chaney, E., 2005. Histogram statistics of local model-relative image regions. Deep Structure, Singularities, and Computer Vision.
4. Chan T., Vese L. Active contours without edges. IEEE Transactions on Image Processing 2001, 10(2):266-277.
5. Chen, S., Radke, R.J., 2009. Level set segmentation with both shape and intensity priors. In: Proceedings of IEEE International Conference on Computer Vision.
6. Chui H., Rangarajan A., Zhang J., Leonard C. Unsupervised learning of an atlas from unlabeled point-sets. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(2):160-172.
7. Collier D., Burnett S., Amin M., Bilton S., et al. Assessment of consistency in contouring of normal-tissue anatomic structures. Journal of Applied Clinical Medical Physics 2003, 4(1):17.
8. Cootes T., Taylor C., Cooper D., Graham J., et al. Active shape models - their training and application. Computer Vision and Image Understanding 1995, 61(1):38-59.
9. Cootes, T., Edwards, G., Taylor, C., 1998. Active appearance models. European Conference on Computer Vision.
10. Costa, M., Delingette, H., Novellas, S., Ayache, N., 2007. Automatic segmentation of bladder and prostate using coupled 3D deformable models. International Conference on Medical Image Computing and Computer-Assisted Intervention.
11. Cremers D., Osher S., Soatto S. Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. International Journal of Computer Vision 2006, 69(3):335-351.
12. Davies, R., Cootes, T., Taylor, C., 2001. A minimum description length approach to statistical shape modelling. Information Processing in Medical Imaging.
13. Foskey M., Davis B., Goyal L., Chang S., et al. Large deformation 3D image registration in image-guided radiation therapy. Physics in Medicine and Biology 2005, 50:5869-5892.
14. Freedman, D., Zhang, T., 2005. Interactive graph cut based segmentation with shape priors. IEEE Conference on Computer Vision and Pattern Recognition.
15. Freedman D., Radke R., Zhang T., Jeong Y., et al. Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging 2005, 24(3):281-292.
16. Freeman, W., Tenenbaum, J., 1997. Learning bilinear models for two-factor problems in vision. IEEE Conference on Computer Vision and Pattern Recognition.
17. Ghosh, P., Mitchell, M., 2006. Segmentation of medical images using a genetic algorithm. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation.
18. Gibou F., Levy D., Cardenas C., Liu P., Boyer A. Partial differential equations-based segmentation for radiotherapy treatment planning. Mathematical Biosciences and Engineering 2005, 2:209-226.
19. Gray H. Anatomy of the Human Body 1918, Lea & Febiger.
20. Haas B., Coradi T., Scholz M., Kunz P., et al. Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies. Physics in Medicine and Biology 2008, 53(6):1751-1772.
21. Heap, T., Hogg, D., 1997. Improving specificity in PDMs using a hierarchical approach. British Machine Vision Conference.
22. Heimann T., Meinzer H. Statistical shape models for 3D medical image segmentation: a review. Medical Image Analysis 2009, 13(4):543-563.
23. Hua C., Lovelock D., Mageras G., Katz M., et al. Development of a semi-automatic alignment tool for accelerated localization of the prostate. International Journal of Radiation Oncology, Biology, Physics 2003, 55(3):811-824.
24. Jeong, Y., Radke, R., 2006. Modeling inter- and intra-patient anatomical variation using a bilinear model. IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis.
25. Jeong Y., Radke R. Reslicing axially sampled 3D shapes using elliptic Fourier descriptors. Medical Image Analysis 2007, 11(2):197-206.
26. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision 1988, 1(4):321-331.
27. Kelemen A., Szekely G., Gerig G. Elastic model-based segmentation of 3-D neuroradiological data sets. IEEE Transactions on Medical Imaging 1999, 18(10):828-839.
28. Klein, S., van der Heide, U., Raaymakers, B., Kotte, A., et al., 2007. Segmentation of the prostate in MR images by atlas matching. IEEE International Symposium on Biomedical Imaging.
29. Leventon, M., Grimson, W., Faugeras, O., 2000. Statistical shape influence in geodesic active contours. IEEE Conference on Computer Vision and Pattern Recognition.
30. Malsch U., Thieke C., Bendl R. Fast elastic registration for adaptive radiotherapy. Medical Image Computing and Computer-Assisted Intervention 2006, 612-619.
31. Martin S., Daanen V., Troccaz J. Atlas-based prostate segmentation using an hybrid registration. International Journal of Computer Assisted Radiology and Surgery 2008, 3(6):485-492.
32. Mazonakis M., Damilakis J., Varveris H., Prassopoulos P., Gourtsoyiannis N. Image segmentation in treatment planning for prostate cancer using the region growing technique. British Journal of Radiology 2001, 74(879):243.
33. Mei, L., Figl, M., Rueckert, D., Darzi, A., Edwards, P., 2008. Statistical shape modelling: how many modes should be retained? Computer Vision and Pattern Recognition Workshops.
34. Mitchell S., Bosch J., Lelieveldt B., Van der Geest R., et al. 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Transactions on Medical Imaging 2002, 21(9):1167-1178.
35. Nain D., Haker S., Bobick A., Tannenbaum A. Multiscale 3-D shape representation and segmentation using spherical wavelets. IEEE Transactions on Medical Imaging 2007, 26(4):598-618.
36. Nocedal J., Wright S. Numerical Optimization 1999, Springer Verlag.
37. Osher S., Sethian J. Fronts propagating with curvature-dependent speed - algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 1988, 79:12-49.
38. Pizer S., Fritsch D., Yushkevich P., Johnson V., Chaney E. Segmentation, registration, and measurement of shape variation via image object shape. IEEE Transactions on Medical Imaging 1999, 18(10):851-865.
39. Romdhani, S., Gong, S., Psarrou, A., et al., 1999. A multi-view nonlinear active shape model using kernel PCA. British Machine Vision Conference.
40. Rousson, M., Khamene, A., Diallo, M., Cell, J., Sauer, P., 2005. Constrained surface evolutions for prostate and bladder segmentation in CT images. Computer Vision for Biomedical Image Applications.
41. Silverman B. Density Estimation for Statistics and Data Analysis 1998, Chapman & Hall/CRC.
42. Smitsmans M., Wolthaus J., Artignan X., De Bois J., et al. Automatic localization of the prostate for on-line or off-line image-guided radiotherapy. International Journal of Radiation Oncology, Biology, Physics 2004, 60(2):623.
43. Staib L., Duncan J. Model-based deformable surface finding for medical images. IEEE Transactions on Medical Imaging 1996, 15(5):720-731.
44. Tsai A., Yezzi A., Wells W., Tempany C., et al. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 2003, 22(2):137-154.
45. Unal, G., Slabaugh, G., 2005. Coupled PDEs for non-rigid registration and segmentation. IEEE Conference on Computer Vision and Pattern Recognition.
46. Wang, F., Vemuri, B., Rangarajan, A., 2006. Groupwise point pattern registration using a novel CDF-based Jensen-Shannon divergence. IEEE Conference on Computer Vision and Pattern Recognition.
47. Xu, W., Amin, S., Haas, O., Burnham, K., Mills, J., 2003. Contour detection by using radial searching for CT images. In: 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine.
48. Zaim, A., Jankun, J., 2005. A Kohonen clustering based approach to segmentation of prostate from TRUS data using gray-level co-occurrence matrix. In: Computer Graphics and Imaging: Eighth IASTED International Conference Proceedings.
49. Zwiggelaar, R., Zhu, Y., Williams, S., 2003. Semi-automatic segmentation of the prostate. In: Proceedings of the First Iberian Conference on Pattern Recognition and Image Analysis.