{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:39:23Z","timestamp":1775119163441,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100014945","name":"Guangzhou Science and Technology Innovation Center","doi-asserted-by":"publisher","award":["No. 202102010251"],"award-info":[{"award-number":["No. 202102010251"]}],"id":[{"id":"10.13039\/501100014945","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":[[2024,3]]},"DOI":"10.1007\/s11517-023-02975-z","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T06:01:50Z","timestamp":1701324110000},"page":"817-827","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SCAN: sequence-based context-aware association network for hepatic vessel segmentation"],"prefix":"10.1007","volume":"62","author":[{"given":"Yinghong","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinfeng","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youfang","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7826-5055","authenticated-orcid":false,"given":"Nian","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"2975_CR1","doi-asserted-by":"crossref","unstructured":"Anwanwan D, Singh SK, Singh S et al (2020) Challenges in liver cancer and possible treatment approaches [J]. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1873(1):188314","DOI":"10.1016\/j.bbcan.2019.188314"},{"issue":"2","key":"2975_CR2","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s11548-011-0624-y","volume":"7","author":"C Schumann","year":"2012","unstructured":"Schumann C, Bieberstein J, Braunewell S et al (2012) Visualization support for the planning of hepatic needle placement [J]. Int J Comput-Assist Radiol Surg 7(2):191\u2013197","journal-title":"Int J Comput-Assist Radiol Surg"},{"issue":"11","key":"2975_CR3","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/TMI.2002.801166","volume":"21","author":"D Selle","year":"2002","unstructured":"Selle D, Preim B, Schenk A et al (2002) Analysis of vasculature for liver surgical planning [J]. IEEE Trans Med Imaging 21(11):1344\u20131357","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2975_CR4","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1148\/radiology.181.2.1924786","volume":"181","author":"M Lafortune","year":"1991","unstructured":"Lafortune M, Madore F, Patriquin H et al (1991) Segmental anatomy of the liver: a sonographic approach to the Couinaud nomenclature [J]. Radiology 181(2):443\u2013448","journal-title":"Radiology"},{"issue":"1","key":"2975_CR5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11548-009-0380-4","volume":"5","author":"A Sboarina","year":"2010","unstructured":"Sboarina A, Foroni RI, Minicozzi A et al (2010) Software for hepatic vessel classification: feasibility study for virtual surgery [J]. Int J Comput Assist Radiol Surg 5(1):39\u201348","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2975_CR6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.compbiomed.2019.04.014","volume":"110","author":"MA Lebre","year":"2019","unstructured":"Lebre MA, Vacavant A, Grand-Brochier M et al (2019) Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme [J]. Comput Biol Med 110:42\u201351","journal-title":"Comput Biol Med"},{"issue":"1","key":"2975_CR7","first-page":"68","volume":"53","author":"L Zhang","year":"2016","unstructured":"Zhang L, Zhang Y (2016) Big data analysis by infinite deep neural networks [J]. J Comput Res Dev 53(1):68\u201379","journal-title":"J Comput Res Dev"},{"issue":"5","key":"2975_CR8","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis [J]. Annu Rev Biomed Eng 19(5):221\u2013248","journal-title":"Annu Rev Biomed Eng"},{"key":"2975_CR9","first-page":"189","volume-title":"Robust retinal vessel segmentation from a data augmentation perspective [C]\/\/International Workshop on Ophthalmic Medical Image Analysis","author":"X Sun","year":"2021","unstructured":"Sun X, Fang H, Yang Y et al (2021) Robust retinal vessel segmentation from a data augmentation perspective [C]\/\/International Workshop on Ophthalmic Medical Image Analysis. Springer, Cham, pp 189\u2013198"},{"key":"2975_CR10","doi-asserted-by":"crossref","unstructured":"Bano S, Vasconcelos F, Shepherd LM et al (2020) Deep placental vessel segmentation for fetoscopic mosaicking [C]\/\/International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham 763\u2013773","DOI":"10.1007\/978-3-030-59716-0_73"},{"key":"2975_CR11","doi-asserted-by":"crossref","unstructured":"Zhou S, Li N, Zhang B et al (2019) Statistical intensity-and shape-modeling to automate cerebrovascular segmentation from TOF-MRA data. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 164\u2013172","DOI":"10.1007\/978-3-030-32245-8_19"},{"issue":"1","key":"2975_CR12","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/TMI.2022.3207093","volume":"42","author":"G Zhao","year":"2022","unstructured":"Zhao G, Liang K, Pan C, Zhang F, Wu X, Hu X, Yu Y (2022) Graph Convolution Based Cross-Network Multiscale Feature Fusion for Deep Vessel Segmentation. IEEE Trans Med Imaging 42(1):183\u2013195","journal-title":"IEEE Trans Med Imaging"},{"key":"2975_CR13","doi-asserted-by":"crossref","unstructured":"Affane A, Lebre MA, Mittal U et al (2020) Literature review of deep learning models for liver vessels reconstruction [C]\/\/2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE 1\u20136","DOI":"10.1109\/IPTA50016.2020.9286639"},{"issue":"2","key":"2975_CR14","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1007\/s00330-003-2161-8","volume":"14","author":"BB Frericks","year":"2004","unstructured":"Frericks BB, Caldarone FC, Nashan B et al (2004) 3D CT modeling of hepatic vessel architecture and volume calculation in living donated liver transplantation [J]. Eur Radiol 14(2):326\u2013333","journal-title":"Eur Radiol"},{"key":"2975_CR15","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.mri.2016.10.021","volume":"36","author":"S Lu","year":"2017","unstructured":"Lu S, Huang H, Liang P et al (2017) Hepatic vessel segmentation using variational level set combined with non-local robust statistics [J]. Magn Reson Imaging 36:180\u2013186","journal-title":"Magn Reson Imaging"},{"key":"2975_CR16","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.compmedimag.2019.05.002","volume":"75","author":"T Kitrungrotsakul","year":"2019","unstructured":"Kitrungrotsakul T, Han XH, Iwamoto Y et al (2019) VesselNet: a deep convolutional neural network with multi pathways for robust hepatic vessel segmentation [J]. Comput Med Imaging Graph 75:74\u201383","journal-title":"Comput Med Imaging Graph"},{"key":"2975_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.compbiomed.2018.08.018","volume":"101","author":"Q Huang","year":"2018","unstructured":"Huang Q, Sun J, Ding H et al (2018) Robust liver vessel extraction using 3D U-Net with variant dice loss function [J]. Comput Biol Med 101:153\u2013162","journal-title":"Comput Biol Med"},{"issue":"7","key":"2975_CR18","doi-asserted-by":"publisher","first-page":"2629","DOI":"10.1109\/JBHI.2020.3042069","volume":"25","author":"Q Yan","year":"2021","unstructured":"Yan Q, Wang B, Zhang W et al (2021) Attention-guided deep neural network with multi-scale feature fusion for liver vessel segmentation [J]. IEEE J Biomed Health Inform 25(7):2629\u20132642","journal-title":"IEEE J Biomed Health Inform"},{"key":"2975_CR19","unstructured":"Zhang D, Liu S, Chaganti S et al (2020) Graph attention network based pruning for reconstructing 3D liver vessel morphology from contrasted CT images [J]. arXiv preprint  arXiv:2003.07999"},{"issue":"3","key":"2975_CR20","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/JBHI.2021.3118104","volume":"26","author":"R Li","year":"2022","unstructured":"Li R, Huang YJ, Chen H et al (2022) 3D graph-connectivity constrained network for hepatic vessel segmentation [J]. IEEE J Biomed Health Inform 26(3):1251\u20131262. https:\/\/doi.org\/10.1109\/JBHI.2021.3118104","journal-title":"IEEE J Biomed Health Inform"},{"key":"2975_CR21","unstructured":"Wu M, Qian Y, Liao X et al (2021) Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention [J]. arXiv preprint  arXiv:2111.03368"},{"key":"2975_CR22","unstructured":"Isensee F, J\u00e4ger PF, Kohl SAA et al (2019) Automated design of deep learning methods for biomedical image segmentation [J]. arXiv preprint  arXiv:1904.08128"},{"key":"2975_CR23","unstructured":"Yi-de M, Qing L, Zhi-Bai Q (2004) Automated image segmentation using improved PCNN model based on cross-entropy [C]\/\/Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004. IEEE 743\u2013746"},{"key":"2975_CR24","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation [C]\/\/2016 Fourth International Conference on 3D Vision (3DV). IEEE 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"issue":"2","key":"2975_CR25","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2019","unstructured":"Karimi D, Salcudean SE (2019) Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks [J]. IEEE Trans Med Imaging 39(2):499\u2013513","journal-title":"IEEE Trans Med Imaging"},{"key":"2975_CR26","doi-asserted-by":"crossref","unstructured":"Scarselli F, Gori M, Ah Chung Tsoi, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE TNNLS 20(1):61\u201380","DOI":"10.1109\/TNN.2008.2005605"},{"key":"2975_CR27","volume-title":"Variational analysis","author":"RT Rockafellar","year":"2009","unstructured":"Rockafellar RT, Wets RJB (2009). Variational analysis, vol 317. Springer Science &amp;amp; Business Media"},{"key":"2975_CR28","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation [C]\/\/International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, Cham 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2975_CR29","unstructured":"Oktay O, Schlemper J, Folgoc LL et al (2018) Attention U-Net: learning where to look for the pancreas [J]. arXiv preprint  arXiv:1804.03999"},{"key":"2975_CR30","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY et al (2018) CBAM: convolutional block attention module [C]\/\/Proceedings of the European conference on computer vision (ECCV). 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"2975_CR31","unstructured":"Park J, Woo S, Lee JY et al (2018) BAM: bottleneck attention module [J]. arKiv preprint arKiv :1807.06514"},{"key":"2975_CR32","doi-asserted-by":"crossref","unstructured":"Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks. arXiv preprint arXiv:1806.09835","DOI":"10.18653\/v1\/P18-1026"},{"key":"2975_CR33","unstructured":"Ballas N, Yao L, Pal C, Courville A (2015) Delving deeper into convolutional networks for learning video representations. arXiv preprint arXiv:1511.06432"},{"key":"2975_CR34","doi-asserted-by":"publisher","first-page":"107471","DOI":"10.1016\/j.knosys.2021.107471","volume":"232","author":"J Su","year":"2021","unstructured":"Su J, Liu Z, Zhang J et al (2021) DV-Net: accurate liver vessel segmentation via dense connection model with D-BCE loss function [J]. Knowl-Based Syst 232:107471","journal-title":"Knowl-Based Syst"},{"key":"2975_CR35","doi-asserted-by":"crossref","unstructured":"Yushkevich P A, Gao Y, Gerig G (2016) ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images [C]\/\/2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE 3342\u20133345","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"2975_CR36","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32"},{"key":"2975_CR37","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS et al (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation [C]\/\/International Conference on Medical Image Computing and Computer-assisted Intervention. Springer, Cham 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"2975_CR38","unstructured":"Cao H, Wang Y, Chen J et al (2021) Swin-unet: Unet-like pure transformer for medical image segmentation [J]. arXiv preprint  arXiv:2105.05537"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02975-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02975-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02975-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T05:14:51Z","timestamp":1708492491000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02975-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2975"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02975-z","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]},"assertion":[{"value":"29 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}