{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:07:26Z","timestamp":1791893246767,"version":"4.3.4"},"reference-count":27,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2020,7,16]],"date-time":"2020-07-16T00:00:00Z","timestamp":1594857600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>High\u2010Dose\u2010Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time consuming and error prone. In this study, we propose a set of deep learning\u2013based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two deep neural networks are developed to address this problem. First, a modified deep U\u2010Net is used to segment the pixels belonging to the brachytherapy needles from the US images. Second, an additional VGG\u201016\u2013based deep convolutional network is combined with the segmentation network to predict the locations of the needle tips. The networks are trained and evaluated on a clinical US images dataset with labeled needle trajectories collected in our hospital (Institutional Review Board approval (IRB 41755)).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The evaluation results show that our method can accurately extract the trajectories of the needles with a resolution of 0.668\u00a0mm and 0.319\u00a0mm in <jats:italic>x<\/jats:italic> and <jats:italic>y<\/jats:italic> direction, respectively. 95.4% of the <jats:italic>x<\/jats:italic> direction and 99.2% of the <jats:italic>y<\/jats:italic> direction have error \u2264 2\u00a0mm. Moreover, the position resolutions of the tips are 0.721, 0.369, and 1.877\u00a0mm in <jats:italic>x<\/jats:italic>,<jats:italic> y<\/jats:italic>, and <jats:italic>z<\/jats:italic> directions, respectively, while 94.2%, 98.3%, and 67.5% of the data have error \u2264 2\u00a0mm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This paper proposed a neural network\u2010based algorithm to segment the brachytherapy needles from the US images and locate the needle tip. It can be used in the HDR brachytherapy to help improve the efficiency and quality of the treatments.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14328","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T11:55:52Z","timestamp":1592308552000},"page":"3797-3805","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep learning applications in automatic needle segmentation in ultrasound\u2010guided prostate brachytherapy"],"prefix":"10.1002","volume":"47","author":[{"given":"Fuyue","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Engineering Physics Tsinghua University Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University Stanford 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hilary","family":"Bagshaw","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University Stanford 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Buyyounouski","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University Stanford 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Stanford University Stanford 94305 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,7,16]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"American Cancer Society Survival Rates for Prostate Cancer https:\/\/www.cancer.org\/cancer\/prostate\u2010cancer\/detection\u2010diagnosis\u2010staging\/survival\u2010rates.html."},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-3016(02)02733-5"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1538-4721(02)00053-3"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11934-008-0009-0"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3157107"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2014.06.007"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12148"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2012.03.002"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s000660050031"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2010.11.007"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2004.09.004"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2013.08.004"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/361237.361242"},{"key":"e_1_2_7_15_1","doi-asserted-by":"crossref","unstructured":"BarvaM UhercikM MariJ\u2010M et\u00a0al.Parallel integral projection transform for straight electrode localization in 3\u2010D ultrasound images IEEE transactions on ultrasonics ferroelectrics and frequency control2008;55:1559\u20131569.","DOI":"10.1109\/TUFFC.2008.833"},{"key":"e_1_2_7_16_1","doi-asserted-by":"crossref","unstructured":"AboofazeliM AbolmaesumiP MousaviP FichtingerG.A new scheme for curved needle segmentation in three\u2010dimensional ultrasound images. In:IEEE International Symposium on Biomedical Imaging: From Nano to Macro IEEE;2009:1067\u20131070.","DOI":"10.1109\/ISBI.2009.5193240"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1591192"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1538231"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2006.07.003"},{"key":"e_1_2_7_20_1","doi-asserted-by":"crossref","unstructured":"ZhouH QiuW DingM ZhangS.Automatic needle segmentation in 3D ultrasound images using 3D improved Hough transform. In:Medical Imaging 2008: Visualization Image\u2010Guided Procedures and Modeling Vol. 6918 International Society for Optics and Photonics;2008:691821.","DOI":"10.1117\/12.770077"},{"key":"e_1_2_7_21_1","unstructured":"KrizhevskyA SutskeverI HintonGE.Imagenet classification with deep convolutional neural networks. In:Advances in neural information processing systems;2012:1097\u20131105."},{"key":"e_1_2_7_22_1","unstructured":"RenS HeK GirshickR SunJ.Faster r\u2010cnn: Towards real\u2010time object detection with region proposal networks. InAdvances in neural information processing systems;2015:91\u201399."},{"key":"e_1_2_7_23_1","doi-asserted-by":"crossref","unstructured":"LongJ ShelhamerE DarrellT.Fully convolutional networks for semantic segmentation. InProceedings of the IEEE conference on computer vision and pattern recognition;2015:3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_7_24_1","unstructured":"GoodfellowI Pouget\u2010AbadieJ MirzaM et\u00a0al.Generative adversarial nets. In:Advances in neural information processing systems;2014:2672\u20132680."},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2948320"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-019-0466-4"},{"key":"e_1_2_7_27_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: Convolutional networks for biomedical image segmentation. In:International Conference on Medical image computing and computer\u2010assisted intervention Springer;2015:234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_7_28_1","doi-asserted-by":"crossref","unstructured":"AboofazeliM AbolmaesumiP MousaviP FichtingerG.A new scheme for curved needle segmentation in three\u2010dimensional ultrasound images. In:2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro;2009:1067\u20131070 ISSN: 1945\u20108452.","DOI":"10.1109\/ISBI.2009.5193240"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.wiley.com\/onlinelibrary\/tdm\/v1\/articles\/10.1002%2Fmp.14328","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14328","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.14328","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.14328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T07:46:41Z","timestamp":1693813601000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.14328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,16]]},"references-count":27,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["10.1002\/mp.14328"],"URL":"https:\/\/doi.org\/10.1002\/mp.14328","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,16]]},"assertion":[{"value":"2020-03-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-02","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-07-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}