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Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods.

Bibliographic reference McGurk, Ross J ; Bowsher, James ; Lee, John ; Das, Shiva K. Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods.. In: Medical physics, Vol. 40, no. 4, p. 042501 (2013)
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  1. Ciernik I.Frank, Dizendorf Elena, Baumert Brigitta G, Reiner Beatrice, Burger Cyrill, Davis J.Bernard, Lütolf Urs M, Steinert Hans C, Von Schulthess Gustav K, Radiation treatment planning with an integrated positron emission and computer tomography (PET/CT): a feasibility study, 10.1016/s0360-3016(03)00346-8
  2. Ford E. C., Herman J., Yorke E., Wahl R. L., 18F-FDG PET/CT for Image-Guided and Intensity-Modulated Radiotherapy, 10.2967/jnumed.108.055780
  3. Zaidi Habib, El Naqa Issam, PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques, 10.1007/s00259-010-1423-3
  4. Nestle, J. Nucl. Med., 46, 1342 (2005)
  5. Schinagl Dominic A.X., Vogel Wouter V., Hoffmann Aswin L., van Dalen Jorn A., Oyen Wim J., Kaanders Johannes H.A.M., Comparison of Five Segmentation Tools for 18F-Fluoro-Deoxy-Glucose–Positron Emission Tomography–Based Target Volume Definition in Head and Neck Cancer, 10.1016/j.ijrobp.2007.07.2333
  6. Erdi Yusuf E., Mawlawi O., Larson Steven M., Imbriaco M., Yeung H., Finn R., Humm John L., Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding, 10.1002/(sici)1097-0142(19971215)80:12+<2505::aid-cncr24>;2-f
  7. Ford Eric C., Kinahan Paul E., Hanlon Lorraine, Alessio Adam, Rajendran Joseph, Schwartz David L., Phillips Mark, Tumor delineation using PET in head and neck cancers: Threshold contouring and lesion volumes : PET-based tumor delineation, 10.1118/1.2361076
  8. Keyes, J. Nucl. Med., 36, 1836 (1995)
  9. Jentzen, J. Nucl. Med., 48, 108 (2007)
  10. Boudraa Abd-El-Ouahab, Champier Jacques, Cinotti Luc, Bordet Jean-Claude, Lavenne Franck, Mallet Jean-Jacques, Delineation and quantitation of brain lesions by fuzzy clustering in Positron Emission Tomography, 10.1016/0895-6111(96)00025-0
  11. Adams R., Bischof L., Seeded region growing, 10.1109/34.295913
  12. Hatt M., Cheze le Rest C., Turzo A., Roux C., Visvikis D., A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET, 10.1109/tmi.2008.2012036
  13. Hatt M, Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, Roux C, Jarritt P, Carson K, Rest C Cheze-Le, Visvikis D, Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET, 10.1088/0031-9155/52/12/010
  14. Geets Xavier, Lee John A., Bol Anne, Lonneux Max, Grégoire Vincent, A gradient-based method for segmenting FDG-PET images: methodology and validation, 10.1007/s00259-006-0363-4
  15. Yang Fei, Grigsby Perry W., Delineation of FDG-PET tumors from heterogeneous background using spectral clustering, 10.1016/j.ejrad.2012.01.001
  16. Artaechevarria X., Munoz-Barrutia A., Ortiz-de-Solorzano C., Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data, 10.1109/tmi.2009.2014372
  17. Kimura F., Shridhar M., Handwritten numerical recognition based on multiple algorithms, 10.1016/0031-3203(91)90094-l
  18. Kittler J., Alkoot F.M., Sum versus vote fusion in multiple classifier systems, 10.1109/tpami.2003.1159950
  19. Lam L., Suen S.Y., Application of majority voting to pattern recognition: an analysis of its behavior and performance, 10.1109/3468.618255
  20. Østergaard Lasse Riis, Larsen Ole Vilhelm, Applying voting to segmentation of MR images, Advances in Pattern Recognition (1998) ISBN:9783540648581 p.795-804, 10.1007/bfb0033304
  21. Therasse Patrick, Arbuck Susan G., Eisenhauer Elizabeth A., Wanders Jantien, Kaplan Richard S., Rubinstein Larry, Verweij Jaap, Van Glabbeke Martine, van Oosterom Allan T., Christian Michaele C., Gwyther Steve G., New Guidelines to Evaluate the Response to Treatment in Solid Tumors, 10.1093/jnci/92.3.205
  22. Schreibmann Eduard, Waller Anthony F., Crocker Ian, Curran Walter, Fox Tim, Voxel clustering for quantifying PET-based treatment response assessment : Voxel clustering for quantifying PET-based treatment response assessment, 10.1118/1.4764900
  23. Nahmias C., Hanna W. T., Wahl L. M., Long M. J., Hubner K. F., Townsend D. W., Time Course of Early Response to Chemotherapy in Non-Small Cell Lung Cancer Patients with 18F-FDG PET/CT, 10.2967/jnumed.106.038513
  24. Larson S, Tumor Treatment Response Based on Visual and Quantitative Changes in Global Tumor Glycolysis Using PET-FDG Imaging The Visual Response Score and the Change in Total Lesion Glycolysis, 10.1016/s1095-0397(99)00016-3
  25. Warfield S.K., Zou K.H., Wells W.M., Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation, 10.1109/tmi.2004.828354
  26. Yamamoto Yuka, Wong Terence Z., Turkington Timothy G., Hawk Thomas C., Coleman R. Edward, Head and Neck Cancer: Dedicated FDG PET/CT Protocol for Detection—Phantom and Initial Clinical Studies, 10.1148/radiol.2433060043
  27. Shepherd Tony, Reply to the Comments on “Comparative Study with New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy”, 10.1109/tmi.2012.2230446
  28. van den Hoff J., Hofheinz F., Comments on “Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy”, 10.1109/tmi.2012.2233209
  29. Hofheinz F, Dittrich S, Pötzsch C, Hoff J van den, Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold, 10.1088/0031-9155/55/4/013
  30. Tylski P., Stute S., Grotus N., Doyeux K., Hapdey S., Gardin I., Vanderlinden B., Buvat I., Comparative Assessment of Methods for Estimating Tumor Volume and Standardized Uptake Value in 18F-FDG PET, 10.2967/jnumed.109.066241
  31. Li Hua, Thorstad Wade L., Biehl Kenneth J., Laforest Richard, Su Yi, Shoghi Kooresh I., Donnelly Eric D., Low Daniel A., Lu Wei, A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours : PET tumor delineation approach, 10.1118/1.2956713
  32. Meyer, J. Nucl. Med., 47, 200 (2006)
  33. Day Ellen, Betler James, Parda David, Reitz Bodo, Kirichenko Alexander, Mohammadi Seyed, Miften Moyed, A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients : A region growing method for tumor segmentation, 10.1118/1.3213099
  34. Hatt Mathieu, Cheze Le Rest Catherine, Albarghach Nidal, Pradier Olivier, Visvikis Dimitris, PET functional volume delineation: a robustness and repeatability study, 10.1007/s00259-010-1688-6
  35. Hofheinz Frank, Langner Jens, Beuthien-Baumann Bettina, Oehme Liane, Steinbach Jörg, Kotzerke Jörg, van den Hoff Jörg, Suitability of bilateral filtering for edge-preserving noise reduction in PET, 10.1186/2191-219x-1-23
  36. Zou Kelly H., Warfield Simon K., Bharatha Aditya, Tempany Clare M.C., Kaus Michael R., Haker Steven J., Wells William M., Jolesz Ferenc A., Kikinis Ron, Statistical validation of image segmentation quality based on a spatial overlap index1, 10.1016/s1076-6332(03)00671-8
  37. Bartko J. J., Measurement and Reliability: Statistical Thinking Considerations, 10.1093/schbul/17.3.483
  38. Zijdenbos A.P., Dawant B.M., Margolin R.A., Palmer A.C., Morphometric analysis of white matter lesions in MR images: method and validation, 10.1109/42.363096
  39. Wahl R. L., Jacene H., Kasamon Y., Lodge M. A., From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors, 10.2967/jnumed.108.057307
  40. Belhassen Saoussen, Zaidi Habib, A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET : Unsupervised segmentation of heterogeneous tumors in PET, 10.1118/1.3301610
  41. Kuncheva Ludmila I., Combining Pattern Classifiers, ISBN:0471210781, 10.1002/0471660264
  42. Oral Sessions, 10.1007/s00259-012-2221-x