Radiation Oncology Journal

The Korean Society for Radiation Oncology (대한방사선종양학회)
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Auto-segmentation of head and neck organs at risk in radiotherapy and its dependence on anatomic similarity

  • Received : 2019.01.09
  • Accepted : 2019.04.15
  • Published : 2019.06.30
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Abstract

Purpose: The aim is to study the dependence of deformable based auto-segmentation of head and neck organs-at-risks (OAR) on anatomy matching for a single atlas based system and generate an acceptable set of contours. Methods: A sample of ten patients in neutral neck position and three atlas sets consisting of ten patients each in different head and neck positions were utilized to generate three scenarios representing poor, average and perfect anatomy matching respectively and auto-segmentation was carried out for each scenario. Brainstem, larynx, mandible, cervical oesophagus, oral cavity, pharyngeal muscles, parotids, spinal cord, and trachea were the structures selected for the study. Automatic and oncologist reference contours were compared using the dice similarity index (DSI), Hausdroff distance and variation in the centre of mass (COM). Results: The mean DSI scores for brainstem was good irrespective of the anatomy matching scenarios. The scores for mandible, oral cavity, larynx, parotids, spinal cord, and trachea were unacceptable with poor matching but improved with enhanced bony matching whereas cervical oesophagus and pharyngeal muscles had less than acceptable scores for even perfect matching scenario. HD value and variation in COM decreased with better matching for all the structures. Conclusion: Improved anatomy matching resulted in better segmentation. At least a similar setup can help generate an acceptable set of automatic contours in systems employing single atlas method. Automatic contours from average matching scenario were acceptable for most structures. Importance should be given to head and neck position during atlas generation for a single atlas based system.

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References

  1. Chao KS, Bhide S, Chen H, et al. Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int J Radiat Oncol Biol Phys 2007;68:1512-21. https://doi.org/10.1016/j.ijrobp.2007.04.037
  2. Reed VK, Woodward WA, Zhang L, et al. Automatic segmentation of whole breast using atlas approach and deformable image registration. Int J Radiat Oncol Biol Phys 2009;73:1493-500. https://doi.org/10.1016/j.ijrobp.2008.07.001
  3. Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys 2014;41:050902. https://doi.org/10.1118/1.4871620
  4. Lim JY, Leech M. Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck. Acta Oncol 2016;55:799-806. https://doi.org/10.3109/0284186X.2016.1173723
  5. Ayyalusamy A, Vellaiyan S, Shanmugam S, et al. Feasibility of offline head & neck adaptive radiotherapy using deformed planning CT electron density mapping on weekly cone beam computed tomography. Br J Radiol 2017;90:20160420. https://doi.org/10.1259/bjr.20160420
  6. Kadoya N. Use of deformable image registration for radiotherapy applications. J Radiol Radiat Ther 2014;2:1042.
  7. Teguh DN, Levendag PC, Voet PW, et al. Clinical validation of atlas-based auto-segmentation of multiple target volumes and normal tissue (swallowing/mastication) structures in the head and neck. Int J Radiat Oncol Biol Phys 2011;81:950-7. https://doi.org/10.1016/j.ijrobp.2010.07.009
  8. Daisne JF, Blumhofer A. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Radiat Oncol 2013;8:154. https://doi.org/10.1186/1748-717X-8-154
  9. Stapleford LJ, Lawson JD, Perkins C, et al. Evaluation of automatic atlas-based lymph node segmentation for headand-neck cancer. Int J Radiat Oncol Biol Phys 2010;77:959-66. https://doi.org/10.1016/j.ijrobp.2009.09.023
  10. Thomson D, Boylan C, Liptrot T, et al. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk. Radiat Oncol 2014;9:173. https://doi.org/10.1186/1748-717X-9-173
  11. Yang J, Beadle BM, Garden AS, et al. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Pract Radiat Oncol 2014;4:e31-7. https://doi.org/10.1016/j.prro.2013.03.003
  12. Sjoberg C, Lundmark M, Granberg C, Johansson S, Ahnesjo A, Montelius A. Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients. Radiat Oncol 2013;8:229. https://doi.org/10.1186/1748-717X-8-229
  13. Raudaschl PF, Zaffino P, Sharp GC, et al. Evaluation of segmentation methods on head and neck CT: autosegmentation challenge 2015. Med Phys 2017;44:2020-36. https://doi.org/10.1002/mp.12197
  14. Han X, Hoogeman MS, Levendag PC, et al. Atlas-based autosegmentation of head and neck CT images. In: Metaxas D, Axel L, Fichtinger G, Szekely G, editors. Medical image computing and computer-assisted intervention. Heidelberg: Springer; 2008. p. 434-41.
  15. Tsuji SY, Hwang A, Weinberg V, Yom SS, Quivey JM, Xia P. Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010;77:707-14. https://doi.org/10.1016/j.ijrobp.2009.06.012
  16. Walker GV, Awan M, Tao R, et al. Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer. Radiother Oncol 2014;112:321-5. https://doi.org/10.1016/j.radonc.2014.08.028
  17. Barley S, Antoine C, Webster G, et al. Atlas-based autocontouring- balancing accuracy with efficiency in OnQ rts. Eur Oncol Haematol 2014;10:98-101. https://doi.org/10.17925/EOH.2014.10.2.98
  18. Wang H, Dong L, O'Daniel J, et al. Validation of an accelerated 'demons' algorithm for deformable image registration in radiation therapy. Phys Med Biol 2005;50:2887-905. https://doi.org/10.1088/0031-9155/50/12/011
  19. Brouwer CL, Steenbakkers RJ, Bourhis J, et al. CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines. Radiother Oncol 2015;117:83-90. https://doi.org/10.1016/j.radonc.2015.07.041
  20. Jameson MG, Holloway LC, Vial PJ, Vinod SK, Metcalfe PE. A review of methods of analysis in contouring studies for radiation oncology. J Med Imaging Radiat Oncol 2010;54:401-10. https://doi.org/10.1111/j.1754-9485.2010.02192.x
  21. Mattiucci GC, Boldrini L, Chiloiro G, et al. Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark? Acta Oncol 2013;52:1417-22. https://doi.org/10.3109/0284186X.2013.813069
  22. Qazi AA, Pekar V, Kim J, Xie J, Breen SL, Jaffray DA. Autosegmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. Med Phys 2011;38:6160-70. https://doi.org/10.1118/1.3654160

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