{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T11:58:33Z","timestamp":1774267113346,"version":"3.50.1"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T00:00:00Z","timestamp":1587427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T00:00:00Z","timestamp":1587427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R21EB028001"],"award-info":[{"award-number":["R21EB028001"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01EB027898"],"award-info":[{"award-number":["R01EB027898"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,6]]},"DOI":"10.1007\/s11548-020-02147-6","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:07:13Z","timestamp":1587442033000},"page":"963-972","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Deep learning-based liver segmentation for fusion-guided intervention"],"prefix":"10.1007","volume":"15","author":[{"given":"Xi","family":"Fang","sequence":"first","affiliation":[]},{"given":"Sheng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Bradford J.","family":"Wood","sequence":"additional","affiliation":[]},{"given":"Pingkun","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,21]]},"reference":[{"issue":"5","key":"2147_CR1","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/s00270-012-0446-5","volume":"35","author":"N Abi-Jaoudeh","year":"2012","unstructured":"Abi-Jaoudeh N, Kruecker J, Kadoury S, Kobeiter H, Venkatesan AM, Levy E, Wood BJ (2012) Multimodality image fusion-guided procedures: technique, accuracy, and applications. Cardio Vasc Interv Radiol 35(5):986\u2013998. https:\/\/doi.org\/10.1007\/s00270-012-0446-5","journal-title":"Cardio Vasc Interv Radiol"},{"issue":"2","key":"2147_CR2","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/34.121791","volume":"14","author":"PJ Besl","year":"1992","unstructured":"Besl PJ, McKay ND (1992) A method for registration of 3-d shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239\u2013256. https:\/\/doi.org\/10.1109\/34.121791","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2147_CR3","unstructured":"Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu CW, Han X, Heng PA, Hesser J, Kadoury S, Konopczynski T, Le M, Li C, Li X, Lipkov\u00e0 J, Lowengrub J, Meine H, Moltz JH, Pal C, Piraud M, Qi X, Qi J, Rempfler M, Roth K, Schenk A, Sekuboyina A, Vorontsov E, Zhou P, H\u00fclsemeyer C, Beetz M, Ettlinger F, Gruen F, Kaissis G, Loh\u00f6fer F, Braren R, Holch J, Hofmann F, Sommer W, Heinemann V, Jacobs C, Mamani GEH, van Ginneken B, Chartrand G, Tang A, Drozdzal M, BenCohen A, Klang E, Amitai MM, Konen E, Greenspan H, Moreau J, Hostettler A, Soler L, Vivanti R, Szeskin A, Lev-Cohain N, Sosna J, Joskowicz L, Menze BH (2019) The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:1901.04056"},{"issue":"3","key":"2147_CR4","doi-asserted-by":"publisher","first-page":"e0117688","DOI":"10.1371\/journal.pone.0117688","volume":"10","author":"SD Billings","year":"2015","unstructured":"Billings SD, Boctor EM, Taylor RH (2015) Iterative most-likely point registration (IMLP): a robust algorithm for computing optimal shape alignment. PloS One 10(3):e0117688","journal-title":"PloS One"},{"key":"2147_CR5","doi-asserted-by":"publisher","unstructured":"Chen X, Zhang R, Yan P (2019) Feature fusion encoder decoder network for automatic liver lesion segmentation. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 430\u2013433, https:\/\/doi.org\/10.1109\/ISBI.2019.8759555","DOI":"10.1109\/ISBI.2019.8759555"},{"key":"2147_CR6","unstructured":"Fang X, Du B, Xu S, Wood BJ, Yan P (2019) Unified multi-scale feature abstraction for medical image segmentation. arXiv preprint arXiv:1910.11456"},{"issue":"4","key":"2147_CR7","doi-asserted-by":"publisher","first-page":"d116","DOI":"10.1007\/s10406-005-0130-9","volume":"15","author":"JR Haaga","year":"2005","unstructured":"Haaga JR (2005) Interventional ct: 30 years\u2019 experience. Eur Radiol Suppl 15(4):d116\u2013d120","journal-title":"Eur Radiol Suppl"},{"key":"2147_CR8","unstructured":"Han X (2017) Automatic liver lesion segmentation using a deep convolutional neural network method. arXiv:1704.07239"},{"key":"2147_CR9","unstructured":"Haskins G, Kruger U, Yan P (2019) Deep learning in medical image registration: a survey. arXiv preprint arXiv:1903.02026"},{"issue":"9","key":"2147_CR10","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2147_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"2147_CR12","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/42.736029","volume":"17","author":"JL Herring","year":"1998","unstructured":"Herring JL, Dawant BM, Maurer CR, Muratore DM, Galloway RL, Fitzpatrick JM (1998) Surface-based registration of ct images to physical space for image-guided surgery of the spine: a sensitivity study. IEEE Transactions on Medical Imaging 17(5):743\u2013752. https:\/\/doi.org\/10.1109\/42.736029","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2147_CR13","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van\u00a0der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"issue":"4","key":"2147_CR14","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1016\/j.jvir.2017.10.021","volume":"29","author":"AK Jones","year":"2018","unstructured":"Jones AK, Dixon RG, Collins JD, Walser EM, Nikolic B (2018) Best practice guidelines for ct-guided interventional procedures. J Vasc Interv Radiol 29(4):518","journal-title":"J Vasc Interv Radiol"},{"key":"2147_CR15","doi-asserted-by":"crossref","unstructured":"Kikinis R, Pieper SD, Vosburgh KG (2014) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, New York, NY, pp 277\u2013289","DOI":"10.1007\/978-1-4614-7657-3_19"},{"key":"2147_CR16","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., pp 1097\u20131105, http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"issue":"12","key":"2147_CR17","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li X, Chen H, Qi X, Dou Q, Fu C, Heng P (2018) H-denseunet: hybrid densely connected unet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imag 37(12):2663\u20132674. https:\/\/doi.org\/10.1109\/TMI.2018.2845918","journal-title":"IEEE Trans Med Imag"},{"key":"2147_CR18","doi-asserted-by":"crossref","unstructured":"Liu S, Xu D, Zhou SK, Pauly O, Grbic S, Mertelmeier T, Wicklein J, Jerebko A, Cai W, Comaniciu D (2018) 3d anisotropic hybrid network: transferring convolutional features from 2d images to 3d anisotropic volumes. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 851\u2013858","DOI":"10.1007\/978-3-030-00934-2_94"},{"issue":"1","key":"2147_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S1361-8415(01)80026-8","volume":"2","author":"J Maintz","year":"1998","unstructured":"Maintz J, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1\u201336. https:\/\/doi.org\/10.1016\/S1361-8415(01)80026-8","journal-title":"Med Image Anal"},{"key":"2147_CR20","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in PyTorch. In: NIPS 2017 Workshop Autodiff"},{"key":"2147_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: MICCAI, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2147_CR22","doi-asserted-by":"publisher","unstructured":"Vorontsov E, Tang A, Pal C, Kadoury S (2018) Liver lesion segmentation informed by joint liver segmentation. In: ISBI, pp 1332\u20131335, https:\/\/doi.org\/10.1109\/ISBI.2018.8363817","DOI":"10.1109\/ISBI.2018.8363817"},{"key":"2147_CR23","unstructured":"Wang L, Lee CY, Tu Z, Lazebnik S (2015) Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:1505.02496"},{"key":"2147_CR24","unstructured":"Yuan Y (2017) Hierarchical convolutional-deconvolutional neural networks for automatic liver and tumor segmentation. arXiv:1710.04540"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02147-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02147-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02147-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:58:57Z","timestamp":1618966737000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02147-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,21]]},"references-count":24,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2020,6]]}},"alternative-id":["2147"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02147-6","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,21]]},"assertion":[{"value":"17 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2020","order":3,"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":"NIH and RPI share intellectual property in the field, and one author receives royalties for licensed patents (BW).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee of where the studies were conducted.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}