Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks

References

• Birenbaum A , Greenspan H. 2017. Multi-view longitudinal CNN for multiple sclerosis lesion segmentation. Eng Appl Artif Intell. 65:111–118.
   Web of Science ®Google Scholar
• Bonferroni C. 1936. Teoria statistica delle classi e calcolo delle probabilita. Pubblicazioni Del R Istituto Superiore Di Scienze Economiche E Commericiali Di Firenze. 8:3–62.
   Google Scholar
• Brouwer CL , Steenbakkers RJ , van Den Heuvel E , Duppen JC , Navran A , Bijl HP , Chouvalova O , Burlage FR , Meertens H , Langendijk JA , et al. 2012. 3D variation in delineation of head and neck organs at risk. Radiother Oncol. 7(1):32.
   Google Scholar
• Brouwer CL , Steenbakkers RJHM , Bourhis J , Budach W , Grau C , Gre- Groire V , van Herk M , Maingon P , Lee A , Nutting C , et al. 2015. CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTIC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines. Radiother Oncol. 117:8390.
   Web of Science ®Google Scholar
• Castadot P , Lee J , Geets X , Gregoire V . 2010. Adaptive radiotherapy for head and neck cancer. Semin Radiat Oncol. 20(2):84–93.
   PubMed Web of Science ®Google Scholar
• Castadot P , Lee J , Parraga A , Geets X , Macq B , Gregoire V . 2008. Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. Radiother Oncol. 89(1):1–12.
   PubMed Web of Science ®Google Scholar
• Christianen MEMC , Langendijk JA , Westerlaan HE , van de Water TA , Bijl HP . 2011. Delineation of organs at risk involved in swallowing for radiotherapy treatment planning. Radiother Oncol. 101(3):394402.
   Web of Science ®Google Scholar
• Daisne JF , Blumhofer A . 2013. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Radiother Oncol. 8:154.
   Google Scholar
• Dice LR . 1945. Measures of the amount of ecologis association between species. Ecology. 26(3):297302.
   Web of Science ®Google Scholar
• Han X , Hoogeman MS , Levendag PC , Hibbard LS , Teguh DN , Voet P , Cowen AC , Wolf TK . 2008. Atlas-based auto-segmentation of head and neck CT images. Med Image Comput Comput-Assist Inverv (MICCAI 2008). 11:434–444.
   Google Scholar
• He K , Zhang X , Ren S , Sun J . 2015. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Proceedings on the IEEE international conference on computer vision; Araucano Park, Las Condes, Chili. p. 1026–1034.
   Google Scholar
• Hu P , Wu F , Peng J , Bao Y , Chen F , Kong D . 2017. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int J Comput Assist Radiol Surg. 12(3):399–411.
   PubMed Web of Science ®Google Scholar
• Ibragimov B , Xing L . 2017. Segmentation of organs-at-risk in head and neck CT images using convolutional neural networks. Med Phys. 44(2):547–557.
   PubMed Web of Science ®Google Scholar
• Kamnitsas K , Ledig C , Newcombe VFJ , Simpson JP , Kane AD , Menon DK , Rueckert D , Glocker B . 2017. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 36:61–78.
   PubMed Web of Science ®Google Scholar
• Kingma DP , Ba J . 2014. Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR); Apr 14–16; San Diego, CA.
   Google Scholar
• Klein S , Staring M , Murphy K , Viergever MA , Pluim JPW . 2010. elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging. 29(1):196–205.
   PubMed Web of Science ®Google Scholar
• Maes F , Vandermeulen D , Suetens P . 2003. Medical image registration using mutual information. Proc IEEE. 91(10):1699–1722.
   Web of Science ®Google Scholar
• Mattiucci GC , Boldrini L , Chiloiro G , DAgostino GR , Chiesa S , De Rose F , Azario L , Pasini D , Gambacorta MA , Balducci M , et al. 2013. Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark? Acta Oncol (Madr). 52(7):14171422.
   Web of Science ®Google Scholar
• Nikolov S , Blackwell S , Mendes R , De Fauw J , Meyer C , Hughes C , Askham H , Romera-Paredes B , Karthikesalingam A , Chu C , et al. 2018. Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. arXiv preprint. arXiv:1809.04430.
   Google Scholar
• Robben D , Bertels J , Willems S , Vandermeulen D , Maes F , Suetens P . 2018. DeepVoxNet: voxel-wise prediction for 3D images. Leuven (Belgium): KU Leuven, Dept. ESAT/PSI; Technical Report: KUL/ESAT/PSI/1801.
   Google Scholar
• Sharp G , Fritscher KD , Pekar V , Peroni M , Shusharina N , Veeraraghavan H , Yang J . 2014. Perspectives on automated image segmentation for radiotherapy. Med Phys. 41(5):0509021.
   Web of Science ®Google Scholar
• Tao C-J , Yi J-L , Chen N-Y , Ren W , Cheng J , Tung S , Kong L , Lin S-J , Pan -J-J , Zhang G-S , et al. 2015. Multi-subject atlas-based auto-segmentation reduces interobserver variation and improves dosimetric parameter consistency for organs at risk in nasopharyngeal carcinoma: a multi-institution clinical study. Radiother Oncol. 115(3):407–411.
   PubMed Web of Science ®Google Scholar
• Van der Veen J , Willems S , Deschuymer S , Robben D , Crijns W , Maes F , Nuyts S . 2019. Benefits of deep learning for delineation of organs at risk in head and neck cancer. Radiother Oncol. 138:68–74.
   PubMed Web of Science ®Google Scholar
• Veiga C , McClelland J , Ricketts K , D’Souza D , Royle G . 2012. Deformable registrations for head and neck cancer adaptive radiotherapy. Proceedings of the first MICCAI workshop on Image-Guidance and Multimodal Dose Planning in Radiation Therapy; Nice, France. p. 66–73.
   Google Scholar
• Vivanti R , Ephrat A , Joskowicz L , Lev-Cohain N , Karaaslan OA , Sosna J . 2015. Automatic liver tumor segmentation in follow-up CT scans: preliminary method and results. International workshop on Patch-based techniques in Medical Imaging (Patch-MI 2015); Munich, Germany. p. 54–61.
   Google Scholar
• Yeghiazaryan V , Voiculescu I . 2015. An overview of current evaluation methods used in medical image segmentation. Oxford: Department of Computer Science, University of Oxford; Tech. Rep. RR-15-08.
   Google Scholar
• Zhang T , Chi Y , Meldolesi E , Yan D . 2007. Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy. Int J Radiat Oncol Biol Phys. 68(2):522–530.
   PubMed Web of Science ®Google Scholar