{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T06:03:00Z","timestamp":1775368980737,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,10,4]],"date-time":"2021-10-04T00:00:00Z","timestamp":1633305600000},"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":["61873239"],"award-info":[{"award-number":["61873239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008990","name":"Zhejiang provincial department of science and technology","doi-asserted-by":"crossref","award":["2020C03074"],"award-info":[{"award-number":["2020C03074"]}],"id":[{"id":"10.13039\/501100008990","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":[[2022,3]]},"DOI":"10.1007\/s11548-021-02506-x","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T04:33:57Z","timestamp":1633408437000},"page":"569-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["GCA-Net: global context attention network for intestinal wall vascular segmentation"],"prefix":"10.1007","volume":"17","author":[{"given":"Sheng","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueting","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiongxiong","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruibiao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,4]]},"reference":[{"issue":"1","key":"2506_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.5946\/ce.2019.061","volume":"53","author":"Y Jung","year":"2020","unstructured":"Jung Y (2020) Endoscopic management of iatrogenic colon perforation. Clin Endosc 53(1):29","journal-title":"Clin Endosc"},{"issue":"3","key":"2506_CR2","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.gie.2008.09.008","volume":"69","author":"G Arora","year":"2009","unstructured":"Arora G, Mannalithara A, Singh G, Gerson LB, Triadafilopoulos G (2009) Risk of perforation from a colonoscopy in adults: a large population-based study. Gastrointest Endosc 69(3):654\u2013664","journal-title":"Gastrointest Endosc"},{"key":"2506_CR3","doi-asserted-by":"crossref","unstructured":"Zheng C, Qian Z, Zhou K, Liu H, Lv D, Zhang W (2017) A novel sensor for real-time measurement of force and torque of colonoscope. In IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society. IEEE, pp 3265\u20133269","DOI":"10.1109\/IECON.2017.8216552"},{"issue":"1","key":"2506_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.1443-1661.2011.01181.x","volume":"24","author":"WB Cheng","year":"2012","unstructured":"Cheng WB, Moser MA, Kanagaratnam S, Zhang WJ (2012) Overview of upcoming advances in colonoscopy. Dig Endosc 24(1):1\u20136","journal-title":"Dig Endosc"},{"key":"2506_CR5","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.jmbbm.2018.11.024","volume":"91","author":"S Johnson","year":"2019","unstructured":"Johnson S, Schultz M, Scholze M, Smith T, Woodfield J, Hammer N (2019) How much force is required to perforate a colon during colonoscopy? an experimental study. J Mech Behav Biomed Mater 91:139\u2013148","journal-title":"J Mech Behav Biomed Mater"},{"key":"2506_CR6","doi-asserted-by":"crossref","unstructured":"Li Y, Gong H, Wu W, Liu G, Chen G (2015) An automated method using hessian matrix and random walks for retinal blood vessel segmentation. In 2015 8th International Congress on Image and Signal Processing (CISP). IEEE, pp 423\u2013427","DOI":"10.1109\/CISP.2015.7407917"},{"issue":"9","key":"2506_CR7","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","volume":"25","author":"JV Soares","year":"2006","unstructured":"Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214\u20131222","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"2506_CR8","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.patcog.2012.08.009","volume":"46","author":"UT Nguyen","year":"2013","unstructured":"Nguyen UT, Bhuiyan A, Park LA, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognit 46(3):703\u2013715","journal-title":"Pattern Recognit"},{"issue":"12","key":"2506_CR9","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1007\/s11548-016-1446-8","volume":"11","author":"E Goceri","year":"2016","unstructured":"Goceri E (2016) Automatic labeling of portal and hepatic veins from mr images prior to liver transplantation. Int J Comput Assist Radiol Surg 11(12):2153\u20132161","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"2506_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s11548-011-0640-y","volume":"7","author":"AH Foruzan","year":"2012","unstructured":"Foruzan AH, Zoroofi RA, Sato Y, Hori M (2012) A hessian-based filter for vascular segmentation of noisy hepatic ct scans. Int J Comput Assist Radiol Surgery 7(2):199\u2013205","journal-title":"Int J Comput Assist Radiol Surgery"},{"key":"2506_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2506_CR12","doi-asserted-by":"crossref","unstructured":"Li L, Verma M, Nakashima Y, Nagahara H, Kawasaki R (2020) Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 3656\u20133665","DOI":"10.1109\/WACV45572.2020.9093621"},{"key":"2506_CR13","doi-asserted-by":"crossref","unstructured":"Wang K, Zhang X, Huang S, Wang Q, Chen F (2020) Ctf-net: Retinal vessel segmentation via deep coarse-to-fine supervision network. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 1237\u20131241","DOI":"10.1109\/ISBI45749.2020.9098742"},{"issue":"10","key":"2506_CR14","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"issue":"7","key":"2506_CR15","doi-asserted-by":"publisher","first-page":"946","DOI":"10.3390\/sym11070946","volume":"11","author":"PM Samuel","year":"2019","unstructured":"Samuel PM, Veeramalai T (2019) Multilevel and multiscale deep neural network for retinal blood vessel segmentation. Symmetry 11(7):946","journal-title":"Symmetry"},{"issue":"12","key":"2506_CR16","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","volume":"12","author":"J Mo","year":"2017","unstructured":"Mo J, Zhang L (2017) Multi-level deep supervised networks for retinal vessel segmentation. Int J Comput Assist Radiol Surg 12(12):2181\u20132193","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"3","key":"2506_CR17","doi-asserted-by":"publisher","first-page":"1958","DOI":"10.1109\/TII.2020.2993842","volume":"17","author":"X Li","year":"2020","unstructured":"Li X, Jiang Y, Li M, Yin S (2020) Lightweight attention convolutional neural network for retinal vessel image segmentation. IEEE Trans Ind Inf 17(3):1958\u20131967","journal-title":"IEEE Trans Ind Inf"},{"issue":"10","key":"2506_CR18","doi-asserted-by":"publisher","first-page":"3008","DOI":"10.1109\/TMI.2020.2983721","volume":"39","author":"S Feng","year":"2020","unstructured":"Feng S, Zhao H, Shi F, Cheng X, Wang M, Ma Y, Xiang D, Zhu W, Chen X (2020) Cpfnet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39(10):3008\u20133018","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"2506_CR19","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11548-021-02344-x","volume":"16","author":"T Tan","year":"2021","unstructured":"Tan T, Wang Z, Du H, Xu J, Qiu B (2021) Lightweight pyramid network with spatial attention mechanism for accurate retinal vessel segmentation. Int J Comput Assist Radiol Surg 16(4):673\u2013682","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2506_CR20","doi-asserted-by":"crossref","unstructured":"Mou L, Zhao Y, Chen L, Cheng J, Gu Z, Hao H, Qi H, Zheng Y, Frangi A, Liu J (2019) Cs-net: channel and spatial attention network for curvilinear structure segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 721\u2013730","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"2506_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2506_CR22","doi-asserted-by":"crossref","unstructured":"Goceri E (2019) Analysis of deep networks with residual blocks and different activation functions: classification of skin diseases. In 2019 Ninth international conference on image processing theory, tools and applications (IPTA). IEEE, pp 1\u20136","DOI":"10.1109\/IPTA.2019.8936083"},{"key":"2506_CR23","doi-asserted-by":"publisher","first-page":"72727","DOI":"10.1109\/ACCESS.2020.2987829","volume":"8","author":"Y Yu","year":"2020","unstructured":"Yu Y, Adu K, Tashi N, Anokye P, Wang X, Ayidzoe MA (2020) Rmaf: Relu-memristor-like activation function for deep learning. IEEE Access 8:72727\u201372741","journal-title":"IEEE Access"},{"key":"2506_CR24","doi-asserted-by":"publisher","first-page":"104458","DOI":"10.1016\/j.compbiomed.2021.104458","volume":"134","author":"E Goceri","year":"2021","unstructured":"Goceri E (2021) Diagnosis of skin diseases in the era of deep learning and mobile technology. Comput Biol Med 134:104458","journal-title":"Comput Biol Med"},{"key":"2506_CR25","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patrec.2020.05.017","volume":"135","author":"M Tanaka","year":"2020","unstructured":"Tanaka M (2020) Weighted sigmoid gate unit for an activation function of deep neural network. Pattern Recognit Lett 135:354\u2013359","journal-title":"Pattern Recognit Lett"},{"key":"2506_CR26","doi-asserted-by":"publisher","first-page":"104118","DOI":"10.1016\/j.compbiomed.2020.104118","volume":"128","author":"E Goceri","year":"2021","unstructured":"Goceri E (2021) Deep learning based classification of facial dermatological disorders. Comput Biol Med 128:104118","journal-title":"Comput Biol Med"},{"key":"2506_CR27","doi-asserted-by":"crossref","unstructured":"EvginGoceri (2019) Skin disease diagnosis from photographs using deep learning. In ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing","DOI":"10.1007\/978-3-030-32040-9_25"},{"issue":"11","key":"2506_CR28","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1109\/TMI.2006.880587","volume":"25","author":"WR Crum","year":"2006","unstructured":"Crum WR, Camara O, Hill DL (2006) Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans Med Imaging 25(11):1451\u20131461","journal-title":"IEEE Trans Med Imaging"},{"key":"2506_CR29","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"2506_CR30","doi-asserted-by":"crossref","unstructured":"Sudre CH, Li W, Vercauteren T, Ourselin S, Cardoso MJ (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 240\u2013248","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"2506_CR31","unstructured":"Kervadec H, Dolz J, Yuan J, Desrosiers C, Granger E, Ayed IB (2019) Constrained deep networks: Lagrangian optimization via log-barrier extensions, arXiv preprint arXiv:1904.04205"},{"issue":"5","key":"2506_CR32","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1049\/iet-ipr.2019.0312","volume":"14","author":"E Goceri","year":"2020","unstructured":"Goceri E (2020) Capsnet topology to classify tumours from brain images and comparative evaluation. IET Image Process 14(5):882\u2013889","journal-title":"IET Image Process"},{"key":"2506_CR33","doi-asserted-by":"publisher","first-page":"117229","DOI":"10.1016\/j.neuroimage.2020.117229","volume":"222","author":"FJ L\u00f3pez-Gonz\u00e1lez","year":"2020","unstructured":"L\u00f3pez-Gonz\u00e1lez FJ, Silva-Rodr\u00edguez J, Paredes-Pacheco J, Ni\u00f1erola-Baiz\u00e1n A, Efthimiou N, Mart\u00edn-Mart\u00edn C, Moscoso A, Ruibal \u00c1, Ro\u00e9-Vellv\u00e9 N, Aguiar P (2020) Intensity normalization methods in brain fdg-pet quantification. Neuroimage 222:117229","journal-title":"Neuroimage"},{"issue":"1","key":"2506_CR34","first-page":"125","volume":"14","author":"E Goceri","year":"2018","unstructured":"Goceri E (2018) Fully automated and adaptive intensity normalization using statistical features for brain mr images. Celal Bayar Univ J Sci 14(1):125\u2013134","journal-title":"Celal Bayar Univ J Sci"},{"issue":"1","key":"2506_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-020-0828-1","volume":"3","author":"YH Chang","year":"2020","unstructured":"Chang YH, Chin K, Thibault G, Eng J, Burlingame E, Gray JW (2020) Restore: Robust intensity normalization method for multiplexed imaging. Commun Biol 3(1):1\u20139","journal-title":"Commun Biol"},{"key":"2506_CR36","unstructured":"Goceri E (2017) Intensity normalization in brain mr images using spatially varying distribution matching. In: 11th International Conference on computer graphics, visualization, computer vision and image processing (CGVCVIP 2017), pp 300\u20134"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02506-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02506-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02506-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T12:17:54Z","timestamp":1645705074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02506-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,4]]},"references-count":36,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["2506"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02506-x","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,4]]},"assertion":[{"value":"16 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 2021","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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","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"}}]}}