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Distance deviation measure of contouring variability

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1. Ayata HB, Güden M, Cemile Ceylan C. Comparison of dose distributions and organs at risk (OAR) doses in conventional tangential technique (CTT) and IMRT plans with different numbers of beam in left-sided breast cancer. RepPract Oncol Radiother 2011; 16: 95-102.10.1016/j.rpor.2011.02.001Search in Google Scholar

2. Petric P, Hudej R, Rogelj P, Blas M, Segedin B, Logar HBZ, et al. Comparison of 3D MRI with high sampling efficiency and 2D multiplanar MRI for contouring in cervix cancer brachytherapy. Radiol Oncol 2012; 46: 242-51.10.2478/v10019-012-0023-1Search in Google Scholar

3. Matthiesen C, Ramgopol R, Seavey J, Ahmad S, Herman T. Intensity modulated radiation therapy (IMRT) for the treatment of unicentric Castlemans disease: a case report and review of the use of radiotherapy in the literature. Radiol Oncol 2012; 46: 265-70.10.2478/v10019-012-0008-0Search in Google Scholar

4. Jameson MG, Holloway LC, Vial PJ, Vinod SK, Metcalfe PE. A review of methods of analysis in contouring studies for radiation oncology. J Med ImagingRadiat Oncol 2010; 54: 401-10.10.1111/j.1754-9485.2010.02192.xSearch in Google Scholar

5. Kouwenhoven E, Giezen M, Struikmans H. Measuring the similarity of target volume delineations independent of the number of observers. Phys MedBiol 2009; 54: 2863-73.10.1088/0031-9155/54/9/018Search in Google Scholar

6. Weltens C, Menten J, Feron M, Bellon E, Demaerel P, Maes F, et al. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiother Oncol 2001; 60: 49-59.10.1016/S0167-8140(01)00371-1Search in Google Scholar

7. Weiss E, Richter S, Krauss T, Metzelthin SI, Hille A, Pradier O, et al. Conformal radiotherapy planning of cervix carcinoma: differences in the delineation of the clinical target volume. A comparison between gynaecologic and radiation oncologists. Radiother Oncol 2003; 67: 87-95.10.1016/S0167-8140(02)00373-0Search in Google Scholar

8. Batumalai V, Koh ES, Delaney GP, Holloway LC, Jameson MG, Papadatos G, et al. Interobserver variability in clinical target volume delineation in tangential breast irradiation: a comparison between radiation oncologists and radiation therapists. Clin Oncol (R Coll Radiol) 2011; 23: 108-13.10.1016/j.clon.2010.10.00421093228Search in Google Scholar

9. Altorjai G, Fotina I, Lütgendorf-Caucig C, Stock M, Pötter R, Georg D, et al. Cone-beam CT-based delineation of stereotactic lung targets: the influence of image modality and target size on interobserver variability. Int J RadiatOncol Biol Phys 2012; 82: e265-72.10.1016/j.ijrobp.2011.03.04221620581Search in Google Scholar

10. Choi HJ, Kim YS, Lee SH, Lee YS, Park G, Jung JH, et al. Inter- and intra-observer variability in contouring of the prostate gland on planning computed tomography and cone beam computed tomography. Acta Oncol 2011; 50: 539-46.10.3109/0284186X.2011.56291621391773Search in Google Scholar

11. Remeijer P, Rasch C, Lebesque JV, van Herk M. A general methodology for three-dimensional analysis of variation in target volume delineation. MedPhys 1999; 26: 931-40.10.1118/1.598485Search in Google Scholar

12. Deurloo KE, Steenbakkers RJ, Zijp LJ, de Bois JA, Nowak PJ, Rasch CR, et al. Quantification of shape variation of prostate and seminal, vesicles during external beam radiotherapy. Int J Radiat Oncol Biol Phys 2005; 61: 228-38.10.1016/j.ijrobp.2004.09.023Search in Google Scholar

13. Steenbakkers RJ, Duppen JC, Fitton I, Deurloo KE, Zijp LJ, Comans EF, et al. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006; 64: 435-48.10.1016/j.ijrobp.2005.06.034Search in Google Scholar

14. Chalana V, Kim Y. A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans Med Imaging 1997; 16: 642-52.10.1109/42.640755Search in Google Scholar

15. Jena R, Kirkby NF, Burton KE, Hoole AC, Tan LT, Burnet NG. A novel algorithm for the morphometric assessment of radiotherapy treatment planning volumes. Br J Radiol 2010; 83: 44-51.10.1259/bjr/27674581Search in Google Scholar

16. Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 2009; 28: 1251-65.10.1109/TMI.2009.2013851Search in Google Scholar

17. van der Put RW, Raaymakers BW, Kerkhof EM, van Vulpen M, Lagendijk JJ. A novel method for comparing 3D target volume delineations in radiotherapy. Phys Med Biol 2008; 53: 2149-59.10.1088/0031-9155/53/8/010Search in Google Scholar

18. Rao M, Stough J, Chi YY, Muller K, Tracton G, Pizer SM, et al. Comparison of human and automatic segmentations of kidneys from CT images. Int JRadiat Oncol Biol Phys 2005; 61: 954-60.10.1016/j.ijrobp.2004.11.014Search in Google Scholar

19. Rosenfeld A, Pfaltz JL. Distance functions on digital pictures. PatternRecognition 1968; 1: 33-61.10.1016/0031-3203(68)90013-7Search in Google Scholar

20. Ye Q-Z. The signed Euclidean distance transform and its applications. PatternRecognition 1988; 1: 495-9. Rome, Italy: 9th International Conference on. Nov. 1988.Search in Google Scholar

21. Maurer CR Jr, Rensheng QI, Raghavan V. A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. Pattern Analysis and Machine Intelligence, IEEE Transactionson 2003; 25: 265-70.10.1109/TPAMI.2003.1177156Search in Google Scholar

22. Xu D, Li H. Euclidean distance transform of digital images in arbitrary dimensions. In: Zhuang Y, Yang S.-Q, Rui Y, He Q, editors. Advances in multimediainformation processing - PCM 2006. Vol. 4261. Lecture notes in computer science. Berlin: Springer Verlag; 2006. pp. 72-9.10.1007/11922162_9Search in Google Scholar

23. Breu H, Gil J, Kirkpatrick D, Werman M. Linear time Euclidean distance transform algorithms. Pattern Analysis and Machine Intelligence, IEEETransactions on1995; 17: 529-33.10.1109/34.391389Search in Google Scholar

24. Wang L, Giesen J, McDonnell KT, Zolliker P, Mueller K. Color design for illustrative visualization. Visualization and Computer Graphics, IEEE Transactionson 2008; 14: 1739-54.10.1109/TVCG.2008.118Search in Google Scholar

25. Petric P, Dimopoulos J, Kirisits C, Berger D, Hudej R, Pötter R. Inter- and intraobserver variation in HR-CTV contouring: Inter-comparison of transverse and paratransverse image orientation in 3D-MRI assisted cervix cancer brachytherapy. Radiother Oncol 2008; 89: 164-71.10.1016/j.radonc.2008.07.030Search in Google Scholar

26. Hurkmans CW, Borger JH, Pieters BR, Russell NS, Jansen EP, Mijnheer BJ. Variability in target volume delineation on CT scans of the breast. Int JRadiat Oncol Biol Phys 2001; 50: 1366-72.10.1016/S0360-3016(01)01635-2Search in Google Scholar

27. Song WY, Chiu B, Bauman GS, Lock M, Rodrigues G, Ash R, et al. Prostate contouring uncertainty in megavoltage computed tomography images acquired with a helical tomotherapy unit during image-guided radiation therapy. Int J Radiat Oncol Biol Phys 2006; 65: 595-607.10.1016/j.ijrobp.2006.01.049Search in Google Scholar

28. Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. Pattern Analysis and Machine Intelligence, IEEETransactions on 1993; 15: 850-63.10.1109/34.232073Search in Google Scholar

29. Bueno G, Déniz O, Salido J, Carrascosa C, Delgado JM. A geodesic deformable model for automatic segmentation of image sequences applied to radiation therapy. Int J Comput Assist Radiol Surg 2011; 6: 341-50.10.1007/s11548-010-0513-9Search in Google Scholar

30. Cardoso JS, Teixeira LF, Cardoso MJ. Automatic breast contour detection in digital photographs. In: Azevedo L, Londral AR, editors. Proceedingsof the First international conference on health informatics, HEALTHINF 2008. Volume 2. Funchal, Madeira, Portugal; January 28-31, 2008. INSTICC - Institute for Systems, Technologies of Information, Control, and Communication; 2008. pp. 91-8. ISBN: 978-989-8111-16-6.Search in Google Scholar

31. Petric P, Hudej R, Rogelj P, Zobec Logar HB. 3D T2-weighted fast recovery fast spin echo sequence MRI for target contouring in cervix cancer brachytherapy. [Abstract]. Brachytherapy (N.Y., N.Y.) 2008; 7: 109-10.Search in Google Scholar

32. Zobec Logar HB, Hudej R, Rogelj P, Petric P. 3D fast recovery fast spin echo MRI for contouring in cervix cancer brachytherapy. [Abstract]. RadiotherOncol 2010; 96(Suppl 1): S205.Search in Google Scholar

33. Segedin B, But Hadzic J, Rogelj P, Sesek M, Zobec Logar HB, Kragelj B, et al. Interobserver variation in MRI and CT based contouring for prostate cancer. [Abstract]. Radiother Oncol 2011; 99(Suppl 1): S285.10.1016/S0167-8140(11)70837-4Search in Google Scholar

eISSN:
1581-3207
ISSN:
1318-2099
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Medicine, Clinical Medicine, Radiology, Internal Medicine, Haematology, Oncology