{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:22:09Z","timestamp":1768414929492,"version":"3.49.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T00:00:00Z","timestamp":1527724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003005","name":"Eindhoven University of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003005","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1007\/s11548-018-1798-3","type":"journal-article","created":{"date-parts":[[2018,5,31]],"date-time":"2018-05-31T09:12:13Z","timestamp":1527757933000},"page":"1321-1333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Robust and semantic needle detection in 3D ultrasound using orthogonal-plane convolutional neural networks"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4542-1354","authenticated-orcid":false,"given":"Arash","family":"Pourtaherian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farhad","family":"Ghazvinian Zanjani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svitlana","family":"Zinger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nenad","family":"Mihajlovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gary C.","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hendrikus H. M.","family":"Korsten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter H. N.","family":"de With","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,5,31]]},"reference":[{"issue":"7","key":"1798_CR1","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TUFFC.2008.833","volume":"55","author":"M Barva","year":"2008","unstructured":"Barva M, Uher\u010d\u00edk M, Mari JM, Kybic J, Duhamel JR, Liebgott H, Hlavac V, Cachard C (2008) Parallel integral projection transform for straight electrode localization in 3-D ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control (UFFC) 55(7):1559\u201369","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control (UFFC)"},{"issue":"6","key":"1798_CR2","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1007\/s11548-016-1402-7","volume":"11","author":"P Beigi","year":"2016","unstructured":"Beigi P, Rohling R, Salcudean SE, Ng GC (2016) Spectral analysis of the tremor motion for needle detection in curvilinear ultrasound via spatiotemporal linear sampling. Int J Comput Assist Radiol Surg 11(6):1183\u20131192","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1798_CR3","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking Atrous convolution for semantic image segmentation. ArXiv e-prints. \n                    ArXiv:1706.05587"},{"issue":"6","key":"1798_CR4","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1145\/358669.358692","volume":"24","author":"MA Fischler","year":"1981","unstructured":"Fischler MA, Bolles RC (1981) Random sample consensus: paradigm for model fitting with applications to image analysis. Commun ACM 24(6):381\u201395","journal-title":"Commun ACM"},{"key":"1798_CR5","unstructured":"Glorot X, Bordes A, Bengio Y (2011) In: Proceedings of the 14th international conference on artificial intelligence and statistics (AISTATS) 2011, vol 15. Fort Lauderdale, FL, USA. pp 315\u2013323. \n                    http:\/\/proceedings.mlr.press\/v15\/glorot11a.html"},{"key":"1798_CR6","unstructured":"Kingma DP, Ba J (2015) ADAM: a method for stochastic optimization. In: International conference on learning representations (ICLR). \n                    ArXiv:1412.6980"},{"key":"1798_CR7","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Conference on computer vision pattern recognition (CVPR). \n                    ArXiv:1411.4038","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1798_CR8","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11548-017-1698-y","volume":"13","author":"C Mwikirize","year":"2018","unstructured":"Mwikirize C, Nosher JL, Hacihaliloglu I (2018) Signal attenuation maps for needle enhancement and localization in 2D ultrasound. Int J Comput Assist Radiol Surg 13:363\u2013374","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"3","key":"1798_CR9","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1016\/j.patcog.2012.09.013","volume":"46","author":"C Papalazarou","year":"2013","unstructured":"Papalazarou C, de With PHN, Rongen P (2013) Sparse-plus-dense-RANSAC for estimation of multiple complex curvilinear models in 2D and 3D. Pattern Recognit 46(3):925\u201335","journal-title":"Pattern Recognit"},{"key":"1798_CR10","first-page":"610","volume":"2","author":"A Pourtaherian","year":"2017","unstructured":"Pourtaherian A, Ghazvinian Zanjani F, Zinger S, Mihajlovic N, Ng G, Korsten H, With P (2017) Improving needle detection in 3D ultrasound using orthogonal-plane convolutional networks. Med Image Comput Comput Assist Interv (MICCAI) 2:610\u2013618","journal-title":"Med Image Comput Comput Assist Interv (MICCAI)"},{"key":"1798_CR11","doi-asserted-by":"crossref","unstructured":"Pourtaherian A, Mihajlovic N, Zinger S, Korsten HHM, de With PHN, Huang J, Ng GC (2016) Automated in-plane visualization of steep needles from 3D ultrasound volumes. In: Proceedings on IEEE international ultrasonics symposium (IUS), pp 1\u20134","DOI":"10.1109\/ULTSYM.2016.7728402"},{"issue":"8","key":"1798_CR12","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TMI.2017.2692302","volume":"36","author":"A Pourtaherian","year":"2017","unstructured":"Pourtaherian A, Scholten H, Kusters L, Zinger S, Mihajlovic N, Kolen A, Zou F, Ng GC, Korsten HHM, de With PHN (2017) Medical instrument detection in 3-dimensional ultrasound data volumes. IEEE Trans Med Imaging (TMI) 36(8):1664\u201375","journal-title":"IEEE Trans Med Imaging (TMI)"},{"key":"1798_CR13","unstructured":"Pourtaherian A, Zinger S, de With PHN, Korsten HHM, Mihajlovic N (2015) Benchmarking of State-of-the-Art needle detection algorithms in 3D ultrasound data volumes. Proc SPIE Med Imaging 9415: 94152B\u20131\u20138"},{"key":"1798_CR14","unstructured":"Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolution network. In: International conference on medical image computing and computer-assisted intervention (MICCAI), pp 599\u2013606"},{"issue":"1","key":"1798_CR15","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/34.655647","volume":"20","author":"H Rowley","year":"1998","unstructured":"Rowley H, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell (PAMI) 20(1):23\u201338","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"issue":"4","key":"1798_CR16","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell (PAMI) 39(4):640\u2013651","journal-title":"IEEE Trans Pattern Anal Mach Intell (PAMI)"},{"key":"1798_CR17","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference learning representations (ICLR). \n                    ArXiv:1409.1556"},{"key":"1798_CR18","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u201358","journal-title":"J Mach Learn Res"},{"key":"1798_CR19","doi-asserted-by":"crossref","unstructured":"Sundaresan V, Bridge CP, Ioannou C, Noble JA (2017) Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks. In: IEEE international conference on biomedical imaging (ISBI), pp. 671\u2013674","DOI":"10.1109\/ISBI.2017.7950609"},{"key":"1798_CR20","unstructured":"Tieleman T, Hinton G (2012) Lect. 6.5-RmsProp: divide gradient by running average of its recent magnitude. COURSERA: Neural Net. for Machine Learning"},{"issue":"12","key":"1798_CR21","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1016\/j.compbiomed.2013.09.020","volume":"43","author":"M Uher\u010d\u00edk","year":"2013","unstructured":"Uher\u010d\u00edk M, Kybic J, Zhao Y, Cachard C, Liebgott H (2013) Line filtering for surgical tool localization in 3D ultrasound images. Comput Biol Med 43(12):2036\u201345","journal-title":"Comput Biol Med"},{"key":"1798_CR22","first-page":"2579","volume":"9","author":"L Maaten van der","year":"2008","unstructured":"van der Maaten L, Hinton G (2008) Visualizing high-dimensional data using t-SNE. J Mach Learn Res 9:2579\u2013605","journal-title":"J Mach Learn Res"},{"key":"1798_CR23","doi-asserted-by":"crossref","unstructured":"Yang X, Yu L, Li S, Wang X, Wang N, Qin J, Ni D, Heng PA (2017) Towards automatic semantic segmentation in volumetric ultrasound. In: Medical image computing and computer-assisted intervention (MICCAI), pp 711\u2013719","DOI":"10.1007\/978-3-319-66182-7_81"},{"key":"1798_CR24","unstructured":"Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convoluions. In: International conference on learning representations (ICLR). \n                    ArXiv:1511.07122"},{"key":"1798_CR25","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision (ECCV), Springer, New York, pp 818\u2013833"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-018-1798-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-018-1798-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-018-1798-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,30]],"date-time":"2019-05-30T21:45:04Z","timestamp":1559252704000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-018-1798-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,31]]},"references-count":25,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,9]]}},"alternative-id":["1798"],"URL":"https:\/\/doi.org\/10.1007\/s11548-018-1798-3","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,31]]},"assertion":[{"value":"19 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2018","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":"This research was conducted in the framework of \u201cImpulse-2 for the healthcare flagship\u2014topic ultrasound\u201d at Eindhoven University of Technology in collaboration with Catharina Hospital Eindhoven and Royal Philips.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"This articles does not contain patient data.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}