{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T16:05:33Z","timestamp":1773849933693,"version":"3.50.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,4,22]],"date-time":"2016-04-22T00:00:00Z","timestamp":1461283200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000156","name":"FRQS","doi-asserted-by":"crossref","award":["26993"],"award-info":[{"award-number":["26993"]}],"id":[{"id":"10.13039\/501100000156","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2017,1]]},"DOI":"10.1007\/s11517-016-1495-8","type":"journal-article","created":{"date-parts":[[2016,4,22]],"date-time":"2016-04-22T10:15:38Z","timestamp":1461320138000},"page":"127-139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Metastatic liver tumour segmentation with a neural network-guided 3D deformable model"],"prefix":"10.1007","volume":"55","author":[{"given":"Eugene","family":"Vorontsov","sequence":"first","affiliation":[]},{"given":"An","family":"Tang","sequence":"additional","affiliation":[]},{"given":"David","family":"Roy","sequence":"additional","affiliation":[]},{"given":"Christopher J.","family":"Pal","sequence":"additional","affiliation":[]},{"given":"Samuel","family":"Kadoury","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,4,22]]},"reference":[{"key":"1495_CR1","unstructured":"Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Ssstrunk S (2010) Slic superpixels. Technical report"},{"issue":"7","key":"1495_CR2","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1007\/s00500-014-1364-z","volume":"19","author":"A Baladhandapani","year":"2014","unstructured":"Baladhandapani A, Nachimuthu DS (2014) Evolutionary learning of spiking neural networks towards quantification of 3D MRI brain tumor tissues. Soft Comput 19(7):1803\u20131816","journal-title":"Soft Comput"},{"issue":"2","key":"1495_CR3","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1148\/radiol.14141180","volume":"275","author":"J Chapiro","year":"2015","unstructured":"Chapiro J, Duran R, Lin M, Schernthaner RE, Wang Z, Gorodetski B, Geschwind JF (2015) Identifying staging markers for hepatocellular carcinoma before transarterial chemoembolization: comparison of three-dimensional quantitative versus nonthree-dimensional imaging markers. Radiology 275(2):438\u2013447","journal-title":"Radiology"},{"key":"1495_CR4","unstructured":"Cohen AB, Diamant I, Klang E, Amitai M, Greenspan H (2014) Automatic detection and segmentation of liver metastatic lesions on serial CT examinations. In: SPIE Medical Imaging, International Society for Optics and Photonics 903519\u2013903519"},{"issue":"4","key":"1495_CR5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4):303\u2013314","journal-title":"Math Control Signals Syst"},{"key":"1495_CR6","doi-asserted-by":"crossref","unstructured":"Dahl G, Sainath T, Hinton G (2013) Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 8609\u20138613","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"1495_CR7","unstructured":"Durst C, Tustison N, Wintermark M, Avants B (2013) Ants and Rboles. In: Proceedings of BRATS Challenge\u2014MICCAI"},{"issue":"2","key":"1495_CR8","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","volume":"45","author":"EA Eisenhauer","year":"2009","unstructured":"Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228\u2013247","journal-title":"Eur J Cancer"},{"key":"1495_CR9","doi-asserted-by":"crossref","unstructured":"Hame Y (2008) Liver tumor segmentation using implicit surface evolution. Midas J. http:\/\/hdl.handle.net\/10380\/1440","DOI":"10.54294\/lwmcho"},{"key":"1495_CR10","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2015) Brain tumor segmentation with deep neural networks. arXiv preprint arXiv:1505.03540"},{"issue":"8","key":"1495_CR11","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1109\/TMI.2009.2013851","volume":"28","author":"T Heimann","year":"2009","unstructured":"Heimann T, Van Ginneken B, Styner M, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G et al (2009) Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans Med Imaging 28(8):1251\u20131265","journal-title":"IEEE Trans Med Imaging"},{"issue":"02","key":"1495_CR12","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"6","author":"S Hochreiter","year":"1998","unstructured":"Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 6(02):107\u2013116","journal-title":"Int J Uncertain Fuzziness Knowl Based Syst"},{"issue":"10","key":"1495_CR13","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1109\/TBME.2014.2325410","volume":"61","author":"M Huang","year":"2014","unstructured":"Huang M, Yang W, Wu Y, Jiang J, Chen W, Feng Q (2014) Brain tumor segmentation based on local independent projection-based classification. IEEE Trans Biomed Eng 61(10):2633\u20132645","journal-title":"IEEE Trans Biomed Eng"},{"issue":"16","key":"1495_CR14","doi-asserted-by":"crossref","first-page":"6459","DOI":"10.1088\/0031-9155\/60\/16\/6459","volume":"60","author":"S Kadoury","year":"2015","unstructured":"Kadoury S, Vorontsov E, Tang A (2015) Metastatic liver tumour segmentation from discriminant grassmannian manifolds. Phys Med Biol 60(16):6459","journal-title":"Phys Med Biol"},{"issue":"4","key":"1495_CR15","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.media.2012.11.006","volume":"17","author":"D Kainmueller","year":"2013","unstructured":"Kainmueller D, Lamecker H, Heller MO, Weber B, Hege HC, Zachow S (2013) Omnidirectional displacements for deformable surfaces. Med Image Anal 17(4):429\u2013441","journal-title":"Med Image Anal"},{"issue":"10","key":"1495_CR16","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1016\/j.fss.2008.11.016","volume":"160","author":"H Khotanlou","year":"2009","unstructured":"Khotanlou H, Colliot O, Atif J, Bloch I (2009) 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160(10):1457\u20131473","journal-title":"Fuzzy Sets Syst"},{"key":"1495_CR17","first-page":"1097","volume-title":"Advances in neural information processing systems 25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates Inc, Red Hook, pp 1097\u20131105"},{"key":"1495_CR18","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807\u2013814"},{"issue":"1","key":"1495_CR19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0885-2308(87)90026-X","volume":"2","author":"DC Plaut","year":"1987","unstructured":"Plaut DC, Hinton GE (1987) Learning sets of filters using back-propagation. Comput Speech Lang 2(1):35\u201361","journal-title":"Comput Speech Lang"},{"key":"1495_CR20","doi-asserted-by":"crossref","unstructured":"Qi Y, Xiong W, Leow WK, Tian Q, Zhou J, Liu J, Han T, Venkatesh SK, Wang SC (2008) Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3d region growing. In: MICCAI Workshop, vol 41, pp 201","DOI":"10.54294\/rofibw"},{"key":"1495_CR21","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.proeng.2012.01.868","volume":"30","author":"A Rajendran","year":"2012","unstructured":"Rajendran A, Dhanasekaran R (2012) Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: a combined approach. Procedia Eng 30:327\u2013333","journal-title":"Procedia Eng"},{"key":"1495_CR22","doi-asserted-by":"crossref","unstructured":"Stawiaski J, Decenciere E, Bidault F (2008) Interactive liver tumor segmentation using graph-cuts and watershed. In: Workshop on 3D segmentation in the clinic: a grand challenge II. Liver tumor segmentation challenge. MICCAI, New York, USA","DOI":"10.54294\/5clvrb"},{"key":"1495_CR23","doi-asserted-by":"crossref","unstructured":"Vorontsov E, Abi-Jaoudeh N, Kadoury S (2014) Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models. In: Yoshida H, Nppi JJ, Saini S (ed) Abdominal imaging. Computational and clinical applications. Number 8676 in Lecture Notes in Computer Science. Springer International Publishing, pp 74\u201383","DOI":"10.1007\/978-3-319-13692-9_7"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-016-1495-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-016-1495-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-016-1495-8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-016-1495-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T11:38:34Z","timestamp":1655552314000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-016-1495-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,22]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["1495"],"URL":"https:\/\/doi.org\/10.1007\/s11517-016-1495-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,22]]}}}