{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T16:19:03Z","timestamp":1764433143651},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T00:00:00Z","timestamp":1517529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1007\/s11063-017-9759-3","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T08:24:56Z","timestamp":1517559896000},"page":"1323-1334","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications"],"prefix":"10.1007","volume":"48","author":[{"given":"Yan","family":"Wang","sequence":"first","affiliation":[]},{"given":"Chen","family":"Zu","sequence":"additional","affiliation":[]},{"given":"Guangliang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zongqing","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Kun","family":"He","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiliu","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,2,2]]},"reference":[{"key":"9759_CR1","doi-asserted-by":"crossref","unstructured":"Stewart BWKP, Wild CP (2014) World cancer report","DOI":"10.12968\/nuwa.2014.10.2.1142051"},{"issue":"10","key":"9759_CR2","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1158\/1055-9965.EPI-06-0353","volume":"15","author":"ET Chang","year":"2006","unstructured":"Chang ET, Adami HO (2006) The enigmatic epidemiology of nasopharyngeal carcinoma. Cancer Epidemiol Biomark Prev 15(10):1765\u20131777","journal-title":"Cancer Epidemiol Biomark Prev"},{"key":"9759_CR3","doi-asserted-by":"publisher","first-page":"74","DOI":"10.3322\/canjclin.55.2.74","volume":"55","author":"D Parkin","year":"2005","unstructured":"Parkin D, Bray F, Ferlay J, Pisani P (2005) Global cancer statistics. CA Cancer J Clin 55:74\u2013108","journal-title":"CA Cancer J Clin"},{"issue":"10","key":"9759_CR4","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1016\/S0006-3223(02)01911-X","volume":"53","author":"A Bertolino","year":"2003","unstructured":"Bertolino A, Frye M, Callicott JH, Mattay VS, Rakow R, Shelton-Repella J et al (2003) Neuronal pathology in the hippocampal area of patients with bipolar disorder: a study with proton magnetic resonance spectroscopic imaging. Biol Psychiatry 53(10):1177\u20131194","journal-title":"Biol Psychiatry"},{"issue":"Pt10","key":"9759_CR5","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1093\/brain\/awp210","volume":"132","author":"R Lodi","year":"2009","unstructured":"Lodi R, Tonon PC, Manners D, Capellari S, Strammiello R, Rinaldi R et al (2009) Magnetic resonance diagnostic markers in clinically sporadic prion disease: a combined brain magnetic resonance imaging and spectroscopy study. Brain 132(Pt10):2669\u201379","journal-title":"Brain"},{"issue":"3","key":"9759_CR6","doi-asserted-by":"publisher","first-page":"603","DOI":"10.3174\/ajnr.A1409","volume":"30","author":"T Satoh","year":"2009","unstructured":"Satoh T, Omi M, Nabeshima M, Onoda K, Date I (2009) Severity analysis of neurovascular contact in patients with trigeminal neuralgia: assessment with the inner view of the 3D MR cisternogram and angiogram fusion imaging. Am J Neuroradiol 30(3):603\u20137","journal-title":"Am J Neuroradiol"},{"issue":"2","key":"9759_CR7","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1088\/0031-9155\/61\/2\/791","volume":"61","author":"Y Wang","year":"2016","unstructured":"Wang Y, Zhang P, An L, Ma G, Kang J, Wu X et al (2016) Predicting standard-dose pet image from low-dose pet and multimodal MR images using mapping-based sparse representation. Phys Med Biol 61(2):791\u2013812","journal-title":"Phys Med Biol"},{"key":"9759_CR8","first-page":"2968","volume":"2015","author":"KW Huang","year":"2015","unstructured":"Huang KW, Zhao ZY, Gong Q, Zha J, Chen L, Yang R (2015) Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy. Eng Med Biol Soc 2015:2968","journal-title":"Eng Med Biol Soc"},{"issue":"3","key":"9759_CR9","first-page":"35","volume":"8","author":"Y Gao","year":"2017","unstructured":"Gao Y, Zhang H, Zhao X, Yan S (2017) Event classification in microblogs via social tracking. ACM Trans Intell Syst Technol (TIST) 8(3):35","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"9759_CR10","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.patcog.2016.09.028","volume":"63","author":"C Zu","year":"2017","unstructured":"Zu C, Wang Z, Zhang D, Liang P, Shi Y, Shen D, Wu G (2017) Robust multi-atlas label propagation by deep sparse representation. Pattern Recogn 63:511\u2013517","journal-title":"Pattern Recogn"},{"issue":"9","key":"9759_CR11","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1109\/TIP.2012.2199502","volume":"21","author":"Y Gao","year":"2012","unstructured":"Gao Y, Wang M, Tao D, Ji R, Dai Q (2012) 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans Image Process 21(9):4290\u20134303","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"9759_CR12","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1108\/AA-11-2015-105","volume":"36","author":"L Chen","year":"2016","unstructured":"Chen L, Chen L, Cui L, Cui L, Huang R, Huang R, Ren Z (2016) Bio-inspired neural network with application to license plate recognition: hysteretic ELM approach. Assembly Autom 36(2):172\u2013178","journal-title":"Assembly Autom"},{"issue":"1","key":"9759_CR13","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","volume":"22","author":"J Shi","year":"2017","unstructured":"Shi J, Zheng X, Li Y, Zhang Q, Ying S (2017) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer\u2019s disease. IEEE J Biomed Health Inform 22(1):173\u2013183","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"9759_CR14","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1109\/JBHI.2016.2602823","volume":"21","author":"J Shi","year":"2017","unstructured":"Shi J, Wu J, Li Y, Zhang Q, Ying S (2017) Histopathological image classification with color pattern random binary hashing based PCANet and matrix-form classifier. IEEE J Biomed Health Inform 21(5):1327\u20131337","journal-title":"IEEE J Biomed Health Inform"},{"key":"9759_CR15","doi-asserted-by":"publisher","unstructured":"Ying S, Wen Z, Shi J, Peng Y, Peng J, Qiao H (2017) Manifold preserving: an intrinsic approach for semisupervised distance metric learning. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2017.2691005","DOI":"10.1109\/TNNLS.2017.2691005"},{"issue":"3","key":"9759_CR16","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1007\/s11063-014-9380-7","volume":"42","author":"JA Ramirez-Quintana","year":"2015","unstructured":"Ramirez-Quintana JA, Chacon-Murguia MI (2015) An adaptive unsupervised neural network based on perceptual mechanism for dynamic object detection in videos with real scenarios. Neural Process Lett 42(3):665\u2013689","journal-title":"Neural Process Lett"},{"issue":"2","key":"9759_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1023\/A:1019933009505","volume":"16","author":"M Brendel","year":"2002","unstructured":"Brendel M, Roska T, B\u00e1rtfai G (2002) Gradient computation of continuous-time cellular neural\/nonlinear networks with linear templates via the CNN universal machine. Neural Process Lett 16(2):111\u2013120","journal-title":"Neural Process Lett"},{"issue":"5","key":"9759_CR18","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","volume":"35","author":"S Pereira","year":"2016","unstructured":"Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240\u20131251","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"9759_CR19","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s11063-015-9420-y","volume":"43","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Zhao D, Sun J, Zou G, Li W (2016) Adaptive convolutional neural network and its application in face recognition. Neural Process Lett 43(2):389\u2013399","journal-title":"Neural Process Lett"},{"key":"9759_CR20","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang W, Li R, Deng H, Wang L, Lin W, Ji S et al (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214\u2013224","journal-title":"Neuroimage"},{"key":"9759_CR21","doi-asserted-by":"crossref","unstructured":"Brebisson AD, Montana G (2015) Deep neural networks for anatomical brain segmentation. In: CVPR bioimage computing workshop, vol 35, pp 20\u201328","DOI":"10.1109\/CVPRW.2015.7301312"},{"key":"9759_CR22","unstructured":"Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. In: MICCAI workshop on multimodal brain tumor segmentation challenge, pp 36\u201339"},{"key":"9759_CR23","unstructured":"Rao V, Sharifi M, Jaiswal A (2015) Brain tumor segmentation with deep learning. In: MICCAI multimodal brain tumor segmentation challenge, pp 56\u201359"},{"issue":"8","key":"9759_CR24","doi-asserted-by":"publisher","first-page":"4662","DOI":"10.1118\/1.3611045","volume":"38","author":"I Fitton","year":"2011","unstructured":"Fitton I, Cornelissen SAP, Duppen JC, Steenbakkers RJHM, Peeters STH, Hoebers FJP et al (2011) Semi- automatic delineation using weighted CT-MRI registered images for radiotherapy of nasopharyngeal cancer. Med Phys 38(8):4662\u20134666","journal-title":"Med Phys"},{"key":"9759_CR25","doi-asserted-by":"crossref","unstructured":"Huang KW, Zhao ZY, Gong Q, Zha J, Chen L, Yang R (2015) Nasopharyngeal carcinoma segmentation via HMRF-EM with maximum entropy. In: Engineering in Medicine and Biology Society, vol 2015, pp 2968","DOI":"10.1109\/EMBC.2015.7319015"},{"issue":"2","key":"9759_CR26","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.ijrobp.2004.09.024","volume":"61","author":"FK Lee","year":"2005","unstructured":"Lee FK, Yeung DK, King AD, Leung SF, Ahuja A (2005) Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images. Int J Radiat Oncol Biol Phys 61(2):608\u2013620","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"9759_CR27","first-page":"18","volume":"2","author":"P Ritthipravat","year":"2008","unstructured":"Ritthipravat P, Tatanun C, Bhongmakapat T, Tuntiyatorn L (2008) Automatic segmentation of nasopharyngeal carcinoma from CT images. Int Conf Biomed Eng Inform 2:18\u201322","journal-title":"Int Conf Biomed Eng Inform"},{"issue":"3","key":"9759_CR28","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/s11548-011-0629-6","volume":"7","author":"W Chanapai","year":"2012","unstructured":"Chanapai W, Bhongmakapat T, Tuntiyatorn L, Ritthipravat P (2012) Nasopharyngeal carcinoma segmentation using a region growing technique. Int J Comput Assist Radiol Surg 7(3):413\u2013422","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"5","key":"9759_CR29","first-page":"683","volume":"42","author":"R Hong","year":"2014","unstructured":"Hong R, Ye S (2014) Segmentation of nasopharyngeal MR medical image base on improved region growing. J Fuzhou Univ (Nat Sci Edn) 42(5):683\u2013688","journal-title":"J Fuzhou Univ (Nat Sci Edn)"},{"key":"9759_CR30","unstructured":"Zhang J, Ma KK, Meng HE, Chong V (2004) Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine. In: International workshop on advanced image technology, pp 207\u2013211"},{"key":"9759_CR31","doi-asserted-by":"crossref","unstructured":"Zhou J, Chan KL, Xu P, Chong VFH (2006) Nasopharyngeal carcinoma lesion segmentation from MR images by support vector machine. In: IEEE international symposium on biomedical imaging: nano to macro, pp 1364\u20131367","DOI":"10.1109\/ISBI.2006.1625180"},{"issue":"2","key":"9759_CR32","first-page":"36","volume":"8","author":"J Zhou","year":"2002","unstructured":"Zhou J, Chong V, Lim TK, Houng J (2002) MRI tumor segmentation for nasopharyngeal carcinoma using knowledge-based fuzzy clustering. Int J Inf Technol 8(2):36\u201345","journal-title":"Int J Inf Technol"},{"issue":"3","key":"9759_CR33","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/s10278-012-9520-4","volume":"26","author":"W Huang","year":"2013","unstructured":"Huang W, Chan KL, Zhou J (2013) Region-based nasopharyngeal carcinoma lesion segmentation from MRI using clustering-and classification-based methods with learning. J Digit Imaging 26(3):472\u2013482","journal-title":"J Digit Imaging"},{"issue":"6","key":"9759_CR34","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310\u201320","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-017-9759-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9759-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9759-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T05:16:21Z","timestamp":1570684581000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-017-9759-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,2]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["9759"],"URL":"https:\/\/doi.org\/10.1007\/s11063-017-9759-3","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,2]]},"assertion":[{"value":"2 February 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}