{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T01:25:18Z","timestamp":1775784318247,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T00:00:00Z","timestamp":1598227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100002790","name":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["20994"],"award-info":[{"award-number":["20994"]}],"id":[{"id":"10.13039\/501100002790","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013873","name":"Government of Ontario","doi-asserted-by":"publisher","award":["IDCD"],"award-info":[{"award-number":["IDCD"]}],"id":[{"id":"10.13039\/100013873","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":[[2020,11]]},"DOI":"10.1007\/s11548-020-02248-2","type":"journal-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T17:02:27Z","timestamp":1598288547000},"page":"1835-1846","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Automatic segmentation of the carotid artery and internal jugular vein from 2D ultrasound images for 3D vascular reconstruction"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9654-9190","authenticated-orcid":false,"given":"Leah A.","family":"Groves","sequence":"first","affiliation":[]},{"given":"Blake","family":"VanBerlo","sequence":"additional","affiliation":[]},{"given":"Natan","family":"Veinberg","sequence":"additional","affiliation":[]},{"given":"Abdulrahman","family":"Alboog","sequence":"additional","affiliation":[]},{"given":"Terry M.","family":"Peters","sequence":"additional","affiliation":[]},{"given":"Elvis C. S.","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,24]]},"reference":[{"key":"2248_CR1","unstructured":"Abdulla W (2017) Mask R-CNN for object detection and instance segmentation on Keras and Tensorflow"},{"issue":"4","key":"2248_CR2","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s11548-017-1665-7","volume":"13","author":"G Ameri","year":"2018","unstructured":"Ameri G, Baxter JSH, Bainbridge D, Peters TM, Chen ECS (2018) Mixed reality ultrasound guidance system: a case study in system development and a cautionary tale. Int J Comput Assist Radiol Surg 13(4):495\u2013505","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2248_CR3","doi-asserted-by":"crossref","unstructured":"Besl PJ, McKay ND (1992) Method for registration of 3-d shapes. In: Sensor fusion IV: control paradigms and data structures, vol 1611. International Society for Optics and Photonics, pp 586\u2013606","DOI":"10.1117\/12.57955"},{"issue":"1","key":"2248_CR4","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1186\/1472-6920-14-168","volume":"14","author":"A Chao","year":"2014","unstructured":"Chao A, Lai CH, Chan KC, Yeh CC, Yeh HM, Fan SZ, Sun WZ (2014) Performance of central venous catheterization by medical students: a retrospective study of students\u2019 logbooks. BMC Med Educ 14(1):168","journal-title":"BMC Med Educ"},{"issue":"6","key":"2248_CR5","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s11548-016-1390-7","volume":"11","author":"ECS Chen","year":"2016","unstructured":"Chen ECS, Peters TM, Ma B (2016) Guided ultrasound calibration: where, how, and how many calibration fiducials. Int J Comput Assist Radiol Surg 11(6):889\u2013898","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"2248_CR6","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.diii.2019.03.002","volume":"100","author":"V Couteaux","year":"2019","unstructured":"Couteaux V, Si-Mohamed S, Nempont O, Lefevre T, Popoff A, Pizaine G, Villain N, Bloch I, Cotten A, Boussel L (2019) Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging 100(4):235\u2013242","journal-title":"Diagn Interv Imaging"},{"key":"2248_CR7","doi-asserted-by":"crossref","unstructured":"Dai Z, Carver E, Liu C, Lee J, Feldman A, Zong W, Pantelic M, Elshaikh M, Wen N (2020) Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic MRI using Mask R-CNN. Adv Radiat Oncol 5:473\u2013481","DOI":"10.1016\/j.adro.2020.01.005"},{"key":"2248_CR8","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision 2015 (ICCV 2015), pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"2","key":"2248_CR9","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/S1051-0443(98)70277-5","volume":"9","author":"AC Gordon","year":"1998","unstructured":"Gordon AC, Saliken John C, Johns D, Owen Richardand\u00a0Gray RR (1998) US-guided puncture of the internal jugular vein: complications and anatomic considerations. J Vasc Interv Radiol 9(2):333\u2013338","journal-title":"J Vasc Interv Radiol"},{"key":"2248_CR10","doi-asserted-by":"crossref","unstructured":"Groves L, Li N, Peters TM, Chen ECS (2019) Towards a mixed-reality first person point of view needle navigation system. In: Essert C, Zhou S, Yap PT, Khan A, Shen D, Liu T, Peters TM, LH Staib (eds) Medical image computing and computer assisted intervention (MICCAI 2019). Springer, Berlin, pp 245\u2013253","DOI":"10.1007\/978-3-030-32254-0_28"},{"key":"2248_CR11","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE international conference on computer vision (ICCV), pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.322"},{"key":"2248_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"10","key":"2248_CR13","doi-asserted-by":"publisher","first-page":"2527","DOI":"10.1109\/TBME.2014.2322864","volume":"61","author":"A Lasso","year":"2014","unstructured":"Lasso A, Heffter T, Rankin A, Pinter C, Ungi T, Fichtinger G (2014) PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Trans Biomed Eng 61(10):2527\u20132537","journal-title":"IEEE Trans Biomed Eng"},{"key":"2248_CR14","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-319-95933-7_4","volume-title":"Intelligent computing theories and application","author":"J Liu","year":"2018","unstructured":"Liu J, Li P (2018) A Mask R-CNN model with improved region proposal network for medical ultrasound image. In: Huang DS, Jo KH, Zhang XL (eds) Intelligent computing theories and application. Springer, Berlin, pp 26\u201333"},{"issue":"11","key":"2248_CR15","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1111\/j.1445-2197.2006.03913.x","volume":"76","author":"A Lo","year":"2006","unstructured":"Lo A, Oehley M, Bartlett A, Adams D, Blyth P, Al-Ali S (2006) Anatomical variations of the common carotid artery bifurcation. ANZ J Surg 76(11):970\u2013972","journal-title":"ANZ J Surg"},{"issue":"3","key":"2248_CR16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.4103\/JETS.JETS_5_18","volume":"11","author":"RL Merritt","year":"2018","unstructured":"Merritt RL, Hachadorian ME, Michaels K, Zevallos E, Mhayamaguru KM, Closser Z, Derr C (2018) The effect of head rotation on the relative vascular anatomy of the neck: implications for central venous access. J Emerg Trauma Shock 11(3):193\u2013196","journal-title":"J Emerg Trauma Shock"},{"key":"2248_CR17","doi-asserted-by":"crossref","unstructured":"Niessen WJ, Bouma CJ, Vincken KL, Viergever MA (2000) Error metrics for quantitative evaluation of medical image segmentation. In: Klette R, Stiehl HS, Viergever MA, Vincken KL (eds) Performance characterization in computer vision. Springer, Berlin, pp 275\u2013284","DOI":"10.1007\/978-94-015-9538-4_22"},{"key":"2248_CR18","doi-asserted-by":"crossref","unstructured":"Prechelt L (2012) Early stopping\u2014but when? In: Neural networks: tricks of the trade, 2nd ed. Springer, Berlin, pp 53\u201367","DOI":"10.1007\/978-3-642-35289-8_5"},{"key":"2248_CR19","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems, Curran Associates, Inc., pp 91\u201399"},{"key":"2248_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9351. Springer, Berlin, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"2248_CR21","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1186\/s13054-017-1814-y","volume":"21","author":"B Saugel","year":"2017","unstructured":"Saugel B, Scheeren TWL, Teboul JL (2017) Ultrasound-guided central venous catheter placement: a structured review and recommendations for clinical practice. Crit Care 21(1):225","journal-title":"Crit Care"},{"key":"2248_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-05088-0","volume-title":"Morphological image analysis","author":"P Soille","year":"2004","unstructured":"Soille P (2004) Morphological image analysis. Springer, Berlin"},{"issue":"3","key":"2248_CR23","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s00270-004-0039-z","volume":"28","author":"UC Turba","year":"2005","unstructured":"Turba UC, Uflacker R, Hannegan C, Selby JB (2005) Anatomic relationship of the internaljugular vein and the common carotid artery applied to percutaneous transjugular procedures. CardioVasc Interv Radiol 28(3):303\u2013306","journal-title":"CardioVasc Interv Radiol"},{"key":"2248_CR24","doi-asserted-by":"crossref","unstructured":"Ukwatta E, Awad J, Buchanan D, Parraga G, Fenster A (2012) Three-dimensional semi-automated segmentation of carotid atherosclerosis from three-dimensional ultrasound images. In: Medical imaging 2012: computer-aided diagnosis, vol 8315, p 83150O. International Society for Optics and Photonics","DOI":"10.1117\/12.912365"},{"key":"2248_CR25","doi-asserted-by":"crossref","unstructured":"Wang W, Liao X, Chen ECS, Moore J, Baxter JSH, Peters Terry M, Bainbridge D (2019) The effects of positioning on the volume\/location of the internal jugular vein using 2-dimensional tracked ultrasound. J Cardiothor Vasc Anesth 34:920\u2013925","DOI":"10.1053\/j.jvca.2019.08.049"},{"key":"2248_CR26","doi-asserted-by":"publisher","unstructured":"Woldeyes DH (2014) Anatomical variations of the common carotid artery bifurcations in relation to the cervical vertebrae in Ethiopia. Anat Physiol Curr Res 4(3). https:\/\/doi.org\/10.4172\/2161-0940.1000143","DOI":"10.4172\/2161-0940.1000143"},{"key":"2248_CR27","doi-asserted-by":"crossref","unstructured":"Xie M, Li Y, Xue Y, Shafritz R, Rahimi SA, Ady JW, Roshan UW (2019) Vessel lumen segmentation in internal carotid artery ultrasounds with deep convolutional neural networks. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 2393\u20132398","DOI":"10.1109\/BIBM47256.2019.8982980"},{"issue":"7","key":"2248_CR28","doi-asserted-by":"publisher","first-page":"mp.13581","DOI":"10.1002\/mp.13581","volume":"46","author":"R Zhou","year":"2019","unstructured":"Zhou R, Fenster A, Xia Y, Spence JD, Ding M (2019) Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images. Med Phys 46(7):mp.13581","journal-title":"Med Phys"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02248-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02248-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T06:49:03Z","timestamp":1696574943000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02248-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,24]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2020,11]]}},"alternative-id":["2248"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02248-2","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,24]]},"assertion":[{"value":"18 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 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":"The authors declare that they have no conflict of interest.","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 and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","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"}}]}}