3,552
Views
51
CrossRef citations to date
0
Altmetric
REVIEW

Use of auto-segmentation in the delineation of target volumes and organs at risk in head and neck

&
Pages 799-806 | Received 08 Nov 2015, Accepted 21 Mar 2016, Published online: 01 Jun 2016

References

  • Stapleford LJ, Lawson JD, Perkins C, et al. Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010;77:959–66.
  • Qazi AA, Pekar V, Kim J, et al. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. Med Phys 2011;38:6160–70.
  • Yang J, Beadle BM, Garden AS, et al. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Prac Radiat Oncol 2014;4:e31–7.
  • 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.
  • Voet PW, Dirkx ML, Teguh DN, et al. Does atlas-based autosegmentation of neck levels require subsequent manual contour editing to avoid risk of severe target underdosage? A dosimetric analysis. Radiother Oncol 2011;98:373–7.
  • 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.
  • Lee NY, Le Q. New developments in radiation therapy for head and neck cancer: intensity modulated radiation therapy and hypoxia targeting. Semin Oncol 2008;35:236–50.
  • Mukesh M, Benson R, Jena R, et al. Interobserver variation in clinical target volume and organs at risk segmentation in post-parotidectomy radiotherapy: can segmentation protocols help? Br J Radiol 2012;85:e530–6.
  • Zhu M, Bzdusek K, Brink C, et al. Multi-institutional quantitative evaluation and clinical validation of smart probabilistic image contouring engine (SPICE) autosegmentation of target structures and normal tissues on computer tomography images in the head and neck, thorax, liver, and male pelvis areas. Int J Radiat Oncol Biol Phys 2013;87:809–16.
  • Pair ML, Du W, Rojas HD, et al. Dosimetric effects of weight loss or gain during volumetric modulated arc therapy and intensity-modulated radiation therapy for prostate cancer. Med Dosim 2013;38:251–4.
  • Tsuji SY, Hwang A, Weinberg V, et al. Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2010;77:707–14.
  • Sun Y, Yu XL, Luo W, et al. Recommendation for a contouring method and atlas of organs at risk in nasopharyngeal carcinoma patients receiving intensity-modulated radiotherapy. Radiother Oncol 2014;110:390–7.
  • The International Commission on Radiation Units and Measurements. J ICRU 2010;10:9–10.
  • 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.
  • Fritscher KD, Peroni M, Zaffino P, et al. Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Med Phys 2014;41:051910.
  • Sykes J. Reflections on the current status of commercial automated segmentation systems in clinical practice. J Med Radiat Sci 2014;61:131–4.
  • 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.
  • Mencarelli A, Kranen SR, Hamming-Vrieze O, et al. Deformable image registration for adaptive radiation therapy of head and neck cancer: accuracy and precision in the presence of tumor changes. Int J Radiat Oncol Biol Phys 2014;90:680–7.
  • Aljabar P, Heckermann RA, Hammers A, et al. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 2009;46:726–38.
  • Sharp G, Fritscher KD, Pekar V, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys 2014;41:050902.
  • Isambert A, Dhermain F, Bidault F, et al. Evaluation of an atlas-based automatic segmentation software for the delineation of brain organs at risk in a radiation therapy clinical context. Radiother Oncol 2008;87:93–9.
  • Chen A, Niermann KJ, Deeley MA, et al. Evaluation of multiple-atlas-based strategies for segmentation of the thyroid gland in head and neck CT images for IMRT. Phys Med Biol 2012;57:93–111.
  • 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.
  • 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.
  • La Macchia M, Fellin F, Amichetti M, et al. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol 2012;7:160.
  • Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials 1996;17:1–12.
  • Mukesh M, Benson R, Jena R, et al. Interobserver variation in clinical target volume and organs at risk segmentation in post-parotidectomy radiotherapy: can segmentation protocols help? Br J Radiol 2012;85:e530–6.
  • Rasch C, Eisbruch A, Remeijer P, et al. Irradiation of paranasal sinus tumors, a delineation and dose comparison study. Int J Radiat Oncol Biol Phys 2002;52:120–7.
  • Levendag PC, Al-Mamgani A, Teguh D, et al. Clinical validation of atlas-based auto-contours of target tissues and critical normal tissue structures in the head & neck. Int J Radiat Oncol Biol Phys 2009;75:a641.
  • Levendag PC, Hoogeman M, Teguh D, et al. Atlas based auto-segmentation of CT images: clinical evaluation of using auto-contouring in high-dose, high precision radiotherapy of cancer in the head, neck. Int J Radiat Oncol Biol Phys 2008;72:s401.
  • Hu K, Lin A, Young A, et al. Timesavings for contour generation in head and neck IMRT: multi-institutional experience with an atlas-based segmentation method. Int J Radiat Oncol Biol Phys 2008;72:S391.
  • Yang J, Amini A, Williamson R, et al. Automatic contouring of brachial plexus using a multi-atlas approach for lung cancer radiation therapy. Prac Radiat Oncol 2013;3:e139–47.
  • Young AV, Wortham A, Wernick I, et al. Atlas-based segmentation improves consistency and decreases time required for contouring postoperative endometrial cancer nodal volumes. Int J Radiat Oncol Biol Phys 2011;79:943–7.
  • Sjoberg C, Lundmark M, Granberg C, et al. Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients. Radiat Oncol 2013;8:229.
  • Anders LC, Stieler F, Siebenlist K, et al. Performance of an atlas-based autosegmentation software for delineation of target volumes for radiotherapy of breast and anorectal cancer. Radiother Oncol 2012;102:68–73.
  • Barley S, Antoine C, Webster G, et al. Atlas-based auto-contouring – balancing accuracy with efficiency in OnQ rts®. Eur Oncol Haematol 2014;10:98–101.
  • Klein S, Heide UA, Lips IM, et al. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys 2008;35:1407–17.
  • Brouwer CL, Steenbakkers RJ, Heuvel E, et al. 3D Variation in delineation of head and neck organs at risk. Radiat Oncol 2012;7:32.
  • Wang H, Garden AS, Zhang L, et al. Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method. Int J Radiat Oncol Biol Phys 2008;72:210–19.
  • Njeh CF. Tumor delineation: The weakest link in the search for accuracy in radiotherapy. J Med Phys 2008;33:136–40.
  • Liyange S, Reznek R. Role of the radiologist in radiotherapy treatment planning. Imag Med 2009;1:3–6.
  • Valentini V, Boldrini L, Damiani A, et al. Recommendations on how to establish evidence from auto-segmentation software in radiotherapy. Radiother Oncol 2014;112:317–20.
  • Faber J, Fonseca LM. How sample size influences research outcomes. Dental Press J Orthodontics 2014;19:27–9.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.