An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy

Medical Image Analysis

Volumn 15, Issue 5, 2011, Pages 772-785

  Lu, Chao ^a   Chelikani, Sudhakar ^b   Papademetris, Xenophon ^a,b   Knisely, Jonathan P ^b   Milosevic, Michael F ^c   Chen, Zhe ^b   Jaffray, David A ^c   Staib, Lawrence H ^a,b   Duncan, James S ^a,b  

^a Yale University   (United States)

^b YALE UNIVERSITY   (United States)

^c PRINCESS MARGARET HOSPITAL   (Canada)

Author keywords

Cervical cancer; Image guided radiotherapy; Image segmentation; Nonrigid registration; Prostate cancer

Indexed keywords

ANATOMICAL CHANGES; AUTOMATIC SEGMENTATIONS; BAYESIAN FRAMEWORKS; CERVICAL CANCERS; CERVIX CANCERS; CLINICAL APPLICATION; CLINICAL PRACTICES; CONDITIONAL MODELS; DOSE DELIVERY; EXTERNAL BEAM RADIOTHERAPY; IMAGE GUIDED RADIOTHERAPY; IMAGE-GUIDED; INTEGRATED ALGORITHM; INTEGRATED APPROACH; MANUAL SEGMENTATION; NEW STRATEGY; NONRIGID REGISTRATION; ORGAN MOTION; ORGAN SEGMENTATION; PELVIC RADIOTHERAPY; PROSTATE CANCERS; QUANTITATIVE RESULT; RIGID REGISTRATION; SOFT TISSUE;

DISEASES; INTEGRATION; RADIOTHERAPY; TISSUE;

IMAGE SEGMENTATION;

ACCURACY; ALGORITHM; ARTICLE; AUTOMATIC IMAGE SEGMENTATION; BAYES THEOREM; BLADDER; CANCER RADIOTHERAPY; CLINICAL ARTICLE; CONTROLLED STUDY; FEMALE; HUMAN; IMAGE GUIDED RADIOTHERAPY; IMAGE PROCESSING; MALE; NONRIGID IMAGE REGISTRATION; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROSTATE; PROSTATE CANCER; QUANTITATIVE ANALYSIS; RADIATION DOSE DISTRIBUTION; RECTUM; REGISTRATION; SENSITIVITY ANALYSIS; THREE DIMENSIONAL IMAGING; TREATMENT PLANNING; UTERINE CERVIX CANCER; UTERUS; VALIDATION STUDY;

ALGORITHMS; BAYES THEOREM; FEMALE; HUMANS; IMAGING, THREE-DIMENSIONAL; MALE; PROSTATIC NEOPLASMS; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOTHERAPY, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; SYSTEMS INTEGRATION; TOMOGRAPHY, X-RAY COMPUTED; UTERINE CERVICAL NEOPLASMS;

EID: 80052177805     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2011.05.010     Document Type: Article

References (43)

1. American Cancer Society, Atlanta, GA, 2010a. How many women get cancer of the cervix? <>. http://www.cancer.org/Cancer/CervicalCancer/DetailedGuide/cervical-cancer-key-statistics/.
2. American Cancer Society, Atlanta, GA, 2010b. What are the key statistics about prostate cancer? <>. http://www.cancer.org/Cancer/ProstateCancer/DetailedGuide/prostate-cancer-key-statistics/.
3. Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological) 1986, 48(3):259-302.
4. Chan T., Vese L. Active contours without edges. IEEE Transactions on Image Processing 2001, 10(2):266-277.
5. Chelikani S., Purushothaman K., Knisely J., Chen Z., Nath R., Bansal R., Duncan J. A gradient feature weighted minimax algorithm for registration of multiple portal images to 3DCT volumes in prostate radiotherapy. International Journal of Radiation Oncology, Biology, Physics 2006, 65(2):535-547.
6. Chen J., Morin O., Aubin M., Bucci M.K., Chuang C.F., Pouliot J. Dose guided radiation therapy with megavoltage cone-beam CT. British Journal of Radiology 2006, 79:S87-S98.
7. Chen T., Kim S., Zhou J., Metaxas D., Rajagopal G., Yue N. 3d meshless prostate segmentation and registration in image guided radiotherapy. Lecture Notes in Computer Science 2009, vol. 5761:43-50. Springer, Berlin/Heidelberg. G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, C. Taylor (Eds.).
8. Chen X., Brady M., Rueckert D. Simultaneous segmentation and registration for medical image. Lecture Notes in Computer Science 2004, vol. 3216:663-670. Springer, Berlin/Heidelberg. C. Barillot, D. Haynor, P. Hellier (Eds.).
9. Choi Y., Lee S. Local injectivity conditions of 2D and 3D uniform cubic B-spline functions. Proceedings of Pacific Graphics 1999, 302-311.
10. Cootes T., Beeston C., Edwards G., Taylor C. A unified framework for atlas matching using active appearance models. Lecture Notes in Computer Science 1999, vol. 1613:322-333. Springer, Berlin/Heidelberg. A. Kuba, M. Saamal, A. Todd-Pokropek (Eds.).
11. Cremers D., Rousson M., Deriche R. A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 2007, 72:195-215.
12. Dawson L.A., Sharpe M.B. Image-guided radiotherapy: rationale, benefits, and limitations. Lancet Oncology 2006, 7:848-858.
13. Foskey M., Davis B., Goyal L., Chang S., Chaney E., Strehl N., Tomei S., Rosenman J., Joshi S. Large deformation three-dimensional image registration in image-guided radiation therapy. Physics in Medicine and Biology 2005, 50:5869-5892.
14. Ghilezan M., Yan D., Liang J., Jaffray D., Wong J., Martinez A. Online image-guided intensity-modulated radiotherapy for prostate cancer: how much improvement can we expect? A theoretical assessment of clinical benefits and potential dose escalation by improving precision and accuracy of radiation delivery. International Journal of Radiation Oncology, Biology, Physics 2004, 60(5):1602-1610.
15. Greene W.H., Chelikani S., Purushothaman K., Chen Z., Knisely J., Staib L.H., Papademetris X., Duncan J.S. A constrained non-rigid registration algorithm for use in prostate image-guided radiotherapy. Lecture Notes in Computer Science 2008, vol. 5241:780-788. Springer, Berlin/Heidelberg. D. Metaxas, L. Axel, G. Fichtinger, G. Szekely (Eds.).
16. Greene W.H., Chelikani S., Purushothaman K., Knisely J., Chen Z., Papademetris X., Staib L.H., Duncan J.S. Constrained non-rigid registration for use in image-guided adaptive radiotherapy. Medical Image Analysis 2009, 13(5):809-817.
17. Heimann T., Meinzer H.-P. Statistical shape models for 3D medical image segmentation: a review. Medical Image Analysis 2009, 13(4):543-563.
18. Hill D.L.G., Batchelor P.G., Holden M., Hawkes D.J. Topical review: medical image registration. Physics in Medicine and Biology 2001, 46:R1-R45.
19. Holden M., Hill D.L.G., Denton E.R.E., Jarosz J.M., Cox T.C.S., Rohlfing T., Goodey J., Hawkes D.J. Voxel similarity measures for 3-D serial MR brain image registration. IEEE Transaction on Medical Imaging 2000, 19(2):94-102.
20. Jaffray D.A., Carlone M., Menard C., Breen S. Image-guided radiation therapy: emergence of MR-guided radiation treatment (MRgRT) systems. Medical Imaging 2010: Physics of Medical Imaging 2010, 7622:1-12.
21. Kirkby C., Stanescu K., Rathee S., Carlone M., Murray B., Fallone B.G. Patient dosimetry for hybrid MRI-radiotherapy systems. Medical Physics 2008, 35(1019).
22. Letourneau D., Wong J.W., Oldham M., Gulam M., Watt L., Jaffray D.A., Siewerdsen J.H., Martinez A.A. Cone-beam-CT guided radiation therapy: technical implementation. Radiotherapy and Oncology 2005, 75:279-286.
23. Leventon, M.E., Grimson, W., Faugeras, O., 2000. Statistical shape influence in geodesic active contours. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2000), pp. 316 -323.
24. Lu C., Chelikani S., Chen Z., Papademetris X., Staib L.H., Duncan J.S. Integrated segmentation and nonrigid registration for application in prostate image-guided radiotherapy. Lecture Notes in Computer Science 2010, vol. 6361:53-60. Springer, Berlin/Heidelberg. T. Jiang, N. Navab, J. Pluim, M. Viergever (Eds.).
25. Lu, C., Chelikani, S., Papademetris, X., Staib, L., Duncan, J., 2010b. Constrained non-rigid registration using Lagrange multipliers for application in prostate radiotherapy. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2010), pp. 133-138.
26. Nag S., Chao C., Martinez A., Thomadsen B. The american brachytherapy society recommendations for low-dose-rate brachytherapy for carcinoma of the cervix. Interantional Journal of Radiation Oncology, Biology, Physics 2002, 52(1):33-48.
27. Osher S., Sethian J.A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 1988, 79(1):12-49.
28. Papademetris, X., Jackowski, M., Rajeevan, N., Constable, R.T., Staib, L.H., 2008. BioImage suite: an integrated medical image analysis suite, Section of Bioimaging Sciences, Dept. of Diagnostic Radiology, Yale School of Medicine. <>. http://www.bioimagesuite.org.
29. Pohl K., Fisher J., Levitt J., Shenton M., Kikinis R., Grimson W., Wells W. A unifying approach to registration, segmentation, and intensity correction. Lecture Notes in Computer Science 2005, vol. 3749:310-318. Springer, Berlin/Heidelberg. J.S. Duncan, G. Gerig (Eds.).
30. Potosky A.L., Legler J., Albertsen P.C., Stanford J.L., Gilliland F.D., Hamilton A.S., Eley J.W., Stephenson R.A., Harlan L.C. Health outcomes after prostatectomy or radiotherapy for prostate cancer: results from the prostate cancer outcomes study. Journal of the National Cancer Institute 2000, 92(19):1582-1592.
31. Rogers D. An Introduction to NURBS: With Historical Perspective 2001, Morgan Kaufmann Publishers, San Francisco. first ed.
32. Rueckert D., Sonoda L., Denton E., Rankin S., Hayes C., Leach M.O., Hill D., Hawkes D.J. Comparison and evaluation of rigid and non-rigid registration of breast MR images. SPIE 1999, 3661:78-88.
33. Rueckert D., Sonoda L.I., Hayes C., Hill D.L.G., Leach M.O., Hawkes D.J. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transaction on Medical Imaging 1999, 18(8):712-721.
34. Siewerdsen J.H., Jaffray D.A. Cone-beam computed tomography with a flat-panel imager: magnitude and effects of X-ray scatter. Medical Physics 2001, 28(2):220-231.
35. Song T., Lee V., Rusinek H., Wong S., Laine A. Integrated four dimensional registration and segmentation of dynamic renal MR images. Lecture Notes in Computer Science 2006, vol. 4191:758-765. Springer, Berlin/Heidelberg. R. Larsen, M. Nielsen, J. Sporring (Eds.).
36. Thilmann C., Nill S., Tucking T., Hoss A., Hesse B., Dietrich L., Bendl R., Rhein B., Haring P., Thieke C., Oelfke U., Debus J., Huber P. Correction of patient positioning errors based on in-line cone beam CTs: clinical implementation and first experiences. Radiation Oncology 2006, 1(16):1-9.
37. Tsai A., Yezzi A., Wells W., Tempany C., Tucker D., Fan A., Grimson W.E., Willsky A. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Transactions on Medical Imaging 2003, 22(2):137-154.
38. Unal, G., Slabaugh, G., june 2005. Coupled PDEs for non-rigid registration and segmentation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 168-175.
39. van de Bunt L., van der Heide U.A., Ketelaars M., de Kort G.A.P., Jurgenliemk-Schulz I.M. Conventional conformal, and intensity-modulated radiation therapy treatment planning of external beam radiotherapy for cervical cancer: the impact of tumor regression. International Journal of Radiation Oncology, Biology, Physics 2006, 64(1):189-196.
40. Varian Medical Systems, Palo Alto, CA., 2010. Varian eclipse treatment planning system. <>. http://www.varian.com.
41. Yang J., Duncan J.S. 3d image segmentation of deformable objects with joint shape-intensity prior models using level sets. Medical Image Analysis 2004, 8(3):285-294.
42. Yezzi A., Zollei L., Kapur T. A variational framework for integrating segmentation and registration through active contours. Medical Image Analysis 2003, 7(2):171-185.
43. Zhu Y., Papademetris X., Sinusas A.J., Duncan J.S. A coupled deformable model for tracking myocardial borders from real-time echocardiography using an incompressibility constraint. Medical Image Analysis 2010, 14(3):429-448.