{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:16:55Z","timestamp":1740143815552,"version":"3.37.3"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T00:00:00Z","timestamp":1664323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 61971118"],"award-info":[{"award-number":["Grant No. 61971118"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11517-022-02644-7","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T14:03:19Z","timestamp":1664373799000},"page":"3325-3340","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography"],"prefix":"10.1007","volume":"60","author":[{"given":"Shuang","family":"Ma","sequence":"first","affiliation":[]},{"given":"Chaolu","family":"Feng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-1273","authenticated-orcid":false,"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yuliang","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Wenjun","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"issue":"20","key":"2644_CR1","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1056\/NEJMoa0909305","volume":"362","author":"RM Greenhalgh","year":"2010","unstructured":"Greenhalgh RM, Powell JT (2010) Endovascular versus open repair of abdominal aortic aneurysm. The New England Journal of Medicine 362(20):1863\u20131871","journal-title":"The New England Journal of Medicine"},{"issue":"2","key":"2644_CR2","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1145\/1031120.1031121","volume":"36","author":"C Kirbas","year":"2004","unstructured":"Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms. ACM Computing Surveys 36(2):81\u2013121","journal-title":"ACM Computing Surveys"},{"issue":"6","key":"2644_CR3","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1016\/j.media.2009.07.011","volume":"13","author":"D Lesage","year":"2009","unstructured":"Lesage D, Angelini ED, Bloch I, Funkalea G (2009) A review of 3d vessel lumen segmentation techniques: Models, features and extraction schemes. Medical Image Analysis 13(6):819\u2013845","journal-title":"Medical Image Analysis"},{"key":"2644_CR4","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia S, De Momi E, Hadji SE, Mattos LS (2018) Blood vessel segmentation algorithms \u2013 review of methods, datasets and evaluation metrics. Computer Methods and Programs in Biomedicine 158:71\u201391","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"12","key":"2644_CR5","doi-asserted-by":"publisher","first-page":"5933","DOI":"10.1109\/TIP.2018.2862346","volume":"27","author":"J Kang","year":"2018","unstructured":"Kang J, Heo S, Hyung WJ, Lim JS, Lee S (2018) 3d active vessel tracking using an elliptical prior. IEEE Transactions on Image Processing 27(12):5933\u20135946","journal-title":"IEEE Transactions on Image Processing"},{"issue":"2","key":"2644_CR6","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1006\/cviu.2000.0866","volume":"80","author":"K Krissian","year":"2000","unstructured":"Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y (2000) Model-based detection of tubular structures in 3d images. Computer Vision and Image Understanding 80(2):130\u2013171","journal-title":"Computer Vision and Image Understanding"},{"issue":"8","key":"2644_CR7","doi-asserted-by":"publisher","first-page":"1994","DOI":"10.1109\/TIP.2007.901204","volume":"16","author":"S Worz","year":"2007","unstructured":"Worz S, Rohr K (2007) Segmentation and quantification of human vessels using a 3-d cylindrical intensity model. IEEE Transactions on Image Processing 16(8):1994\u20132004","journal-title":"IEEE Transactions on Image Processing"},{"issue":"35","key":"2644_CR8","doi-asserted-by":"publisher","DOI":"10.1117\/12.845638","volume":"7625","author":"MA Gulsun","year":"2010","unstructured":"Gulsun MA, Tek H (2010) Segmentation of carotid arteries by graph-cuts using centerline models. Proceedings of SPIE 7625(35):762530","journal-title":"Proceedings of SPIE"},{"key":"2644_CR9","doi-asserted-by":"crossref","unstructured":"Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Intervention, Springer-Verlag, pp 130\u2013137","DOI":"10.1007\/BFb0056195"},{"issue":"1","key":"2644_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.media.2015.07.001","volume":"26","author":"R Moreno","year":"2015","unstructured":"Moreno R, Smedby O (2015) Gradient-based enhancement of tubular structures in medical images. Medical Image Analysis 26(1):19\u201329","journal-title":"Medical Image Analysis"},{"issue":"6","key":"2644_CR11","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.3233\/BME-141149","volume":"24","author":"J Yang","year":"2014","unstructured":"Yang J, Ma S, Sun Q, Tan W, Zhao D (2014) Improved hessian multiscale enhancement filter. Bio-medical materials and engineering 24(6):3267\u20133275","journal-title":"Bio-medical materials and engineering"},{"issue":"1","key":"2644_CR12","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TMI.2003.819920","volume":"23","author":"O Wink","year":"2004","unstructured":"Wink O, Niessen WJ, Viergever MA (2004) Multiscale vessel tracking. IEEE Transactions on Medical Imaging 23(1):130\u2013133","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"10","key":"2644_CR13","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.1109\/TMI.2015.2425535","volume":"34","author":"S Cetin","year":"2015","unstructured":"Cetin S, Unal G (2015) A higher-order tensor vessel tractography for segmentation of vascular structures. IEEE Transactions on Medical Imaging 34(10):2172\u20132185","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2644_CR14","first-page":"602","volume":"11","author":"MA Gulsun","year":"2008","unstructured":"Gulsun MA, Tek H (2008) Robust vessel tree modeling. Medical Image Computing and Computer-Assisted Intervention 11:602\u2013611","journal-title":"Medical Image Computing and Computer-Assisted Intervention"},{"issue":"2","key":"2644_CR15","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.media.2009.12.003","volume":"14","author":"O Friman","year":"2010","unstructured":"Friman O, Hindennach M, K\u00fchnel C, Peitgen HO (2010) Multiple hypothesis template tracking of small 3d vessel structures. Medical image analysis 14(2):160\u2013171","journal-title":"Medical image analysis"},{"issue":"3","key":"2644_CR16","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1109\/TMI.2006.891503","volume":"26","author":"R Manniesing","year":"2007","unstructured":"Manniesing R, Viergever MA, Niessen WJ (2007) Vessel axis tracking using topology constrained surface evolution. IEEE Transactions on Medical Imaging 26(3):309\u2013316","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"8","key":"2644_CR17","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1109\/TIP.2015.2417683","volume":"24","author":"Y Cheng","year":"2015","unstructured":"Cheng Y, Hu X, Wang J, Wang Y, Tamura S (2015) Accurate vessel segmentation with constrained b-snake. IEEE Transactions on Image Processing 24(8):2440\u20132455","journal-title":"IEEE Transactions on Image Processing"},{"key":"2644_CR18","doi-asserted-by":"crossref","unstructured":"Li C, Kao C, Gore JC, Ding Z (2007) Implicit active contours driven by local binary fitting energy. In: Computer Vision and Pattern Recognition, pp 1\u20137","DOI":"10.1109\/CVPR.2007.383014"},{"key":"2644_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.cmpb.2018.10.010","volume":"166","author":"M Chung","year":"2018","unstructured":"Chung M, Lee J, Chung JW, Shin YG (2018) Accurate liver vessel segmentation via active contour model with dense vessel candidates. Computer Methods and Programs in Biomedicine 166:61\u201375. https:\/\/doi.org\/10.1016\/j.cmpb.2018.10.010","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2644_CR20","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1109\/ICPR.2002.1044862","volume":"1","author":"T Deschamps","year":"2002","unstructured":"Deschamps T, Cohen L (2002) Fast extraction of tubular and tree 3d surfaces with front propagation methods. International Conference on Pattern Recognition 1:731\u2013734","journal-title":"International Conference on Pattern Recognition"},{"key":"2644_CR21","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"issue":"3","key":"2644_CR22","doi-asserted-by":"publisher","first-page":"2069","DOI":"10.1007\/s11548-019-02062-5","volume":"14","author":"M Oda","year":"2019","unstructured":"Oda M, Roth HR, Kitasaka T, Misawa K, Mori K (2019) Abdominal artery segmentation method from ct volumes using fully convolutional neural network. International Journal of Computer Assisted Radiology and Surgery 14(3):2069\u20132081","journal-title":"International Journal of Computer Assisted Radiology and Surgery"},{"issue":"9","key":"2644_CR23","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","volume":"65","author":"Z Yan","year":"2018","unstructured":"Yan Z, Yang X, Cheng K (2018) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Transactions on Biomedical Engineering 65(9):1912\u20131923. https:\/\/doi.org\/10.1109\/TBME.2018.2828137","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"2644_CR24","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Springer International Publishing, Cham, pp 234\u2013241"},{"key":"2644_CR25","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: Learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Springer International Publishing, Cham, pp 424\u2013432"},{"key":"2644_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107471","volume":"232","author":"J Su","year":"2021","unstructured":"Su J, Liu Z, Zhang J, Sheng VS, Song Y, Zhu Y, Liu Y (2021) Dv-net: Accurate liver vessel segmentation via dense connection model with d-bce loss function. Knowledge-Based Systems 232:107471. https:\/\/doi.org\/10.1016\/j.knosys.2021.107471","journal-title":"Knowledge-Based Systems"},{"key":"2644_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101799","volume":"86","author":"J Gu","year":"2020","unstructured":"Gu J, Fang Z, Gao Y, Tian F (2020) Segmentation of coronary arteries images using global feature embedded network with active contour loss. Computerized Medical Imaging and Graphics 86:101799. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101799","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"2644_CR28","doi-asserted-by":"publisher","first-page":"7192","DOI":"10.1109\/TIP.2020.2999854","volume":"29","author":"A Nazir","year":"2020","unstructured":"Nazir A, Cheema MN, Sheng B, Li H, Li P, Yang P, Jung Y, Qin J, Kim J, Feng DD (2020) Off-enet: An optimally fused fully end-to-end network for automatic dense volumetric 3d intracranial blood vessels segmentation. IEEE Transactions on Image Processing 29:7192\u20137202. https:\/\/doi.org\/10.1109\/TIP.2020.2999854","journal-title":"IEEE Transactions on Image Processing"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02644-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02644-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02644-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T03:40:33Z","timestamp":1665114033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02644-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,28]]},"references-count":28,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["2644"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02644-7","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2022,9,28]]},"assertion":[{"value":"18 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}}]}}