{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:52:19Z","timestamp":1786981939803,"version":"3.56.0"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Development Fund of Macao SAR","award":["0016\/2019\/A1"],"award-info":[{"award-number":["0016\/2019\/A1"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-022-02803-z","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T08:05:20Z","timestamp":1670227520000},"page":"653-661","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Jigsaw training-based background reverse attention transformer network for guidewire segmentation"],"prefix":"10.1007","volume":"18","author":[{"given":"Guifang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-1417","authenticated-orcid":false,"given":"Hon-Cheng","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"2803_CR1","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/s11548-021-02529-4","volume":"17","author":"S Jiang","year":"2022","unstructured":"Jiang S, Teng S, Lu J, Wang C, Wen T, Zhu J, Teng G (2022) PixelTopoIS: a pixel-topology-coupled guidewire tip segmentation framework for robot-assisted intervention. Int J Comput Assist Radiol Surg 17:329\u2013341","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2803_CR2","first-page":"103","volume-title":"IEEE 13th international symposium on biomedical imaging (ISBI)","author":"BJ Chen","year":"2016","unstructured":"Chen BJ, Wu Z, Sun S, Zhang D, Chen T (2016) Guidewire tracking using a novel sequential segment optimization method in interventional X-ray videos. IEEE 13th international symposium on biomedical imaging (ISBI). Czech Republic, Prague, pp 103\u2013106"},{"key":"2803_CR3","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.media.2017.02.001","volume":"38","author":"A Vandini","year":"2017","unstructured":"Vandini A, Glocker B, Hamady M, Yang GZ (2017) Robust guidewire tracking under large deformations combining segment-like features (SEGlets). Med Image Anal 38:150\u2013164","journal-title":"Med Image Anal"},{"issue":"3","key":"2803_CR4","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1109\/TMI.2012.2228879","volume":"32","author":"H Heibel","year":"2013","unstructured":"Heibel H, Glocker B, Groher M, Pfister M, Navab N (2013) Interventional tool tracking using discrete optimization. IEEE Trans Med Imaging 32(3):544\u2013555","journal-title":"IEEE Trans Med Imaging"},{"key":"2803_CR5","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013455","journal-title":"Nature"},{"key":"2803_CR6","first-page":"1","volume-title":"2019 international joint conference on neural networks (IJCNN)","author":"YJ Zhou","year":"2019","unstructured":"Zhou YJ, Xie XL, Bian GB, Hou ZG, Wu YD, Liu SQ, Wang JX (2019) Fully automatic dual-guidewire segmentation for coronary bifurcation lesion. 2019 international joint conference on neural networks (IJCNN). Budapest, Hungary, pp 1\u20136"},{"key":"2803_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101734","volume":"83","author":"YJ Zhou","year":"2020","unstructured":"Zhou YJ, Xie XL, Zhou XH, Liu SQ, Bian GB, Hou ZG (2020) Pyramid attention recurrent networks for real-time guidewire segmentation and tracking in intraoperative X-ray fluoroscopy. Comput Med Imag Graph 83:101734","journal-title":"Comput Med Imag Graph"},{"key":"2803_CR8","doi-asserted-by":"crossref","unstructured":"Zhou YJ, Xie XL, Hou ZG, Bian GB, Liu SQ, Zhou XH (2020) FRR-NET: fast recurrent residual networks for real-time catheter segmentation and tracking in endovascular aneurysm repair. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). Iowa City, pp 961\u2013964","DOI":"10.1109\/ISBI45749.2020.9098632"},{"key":"2803_CR9","doi-asserted-by":"crossref","unstructured":"Wu YD, Xie XL, Bian GB, Hou ZG, Cheng XR, Chen S, Wang QL (2018) Automatic guidewire tip segmentation in 2D X-ray fluoroscopy using convolution neural networks. In: International joint conference on neural networks (IJCNN), Rio de Janeiro, Brazil, pp 1\u20137","DOI":"10.1109\/IJCNN.2018.8489337"},{"key":"2803_CR10","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, Las Vegas, Nevada, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2803_CR11","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Salt Lake City, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2803_CR12","doi-asserted-by":"crossref","unstructured":"Guo S, Tang S, Zhu J, Fan J, Ai D, Song H, Yang J (2019) Improved U-net for guidewire tip segmentation in X-ray fluoroscopy images. In: Proceedings of the 2019 3rd international conference on advances in image processing, Chengdu, China, pp 55\u201359","DOI":"10.1145\/3373419.3373449"},{"key":"2803_CR13","doi-asserted-by":"publisher","first-page":"2002","DOI":"10.1109\/TMI.2021.3069998","volume":"40","author":"RQ Li","year":"2021","unstructured":"Li RQ, Xie XL, Zhou XH, Liu SQ, Ni ZL, Zhou YJ, Hou ZG (2021) Real-time multi-guidewire endpoint localization in fluoroscopy images. IEEE Trans Med Imag 40:2002\u20132014","journal-title":"IEEE Trans Med Imag"},{"key":"2803_CR14","first-page":"215","volume-title":"International conference on artificial intelligence in information and communication (ICAIIC)","author":"I Ullah","year":"2019","unstructured":"Ullah I, Chikontwe P, Park SH (2019) Guidewire tip tracking using U-Net with shape and motion constraints. International conference on artificial intelligence in information and communication (ICAIIC). Okinawa, Japan, pp 215\u2013217"},{"key":"2803_CR15","first-page":"234","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. International conference on medical image computing and computer-assisted intervention. Munich, Germany, pp 234\u2013241"},{"key":"2803_CR16","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Polosukhin I (2017) Polosukhin, Attention is all you need. Advances in neural information processing systems. Long Beach, California, USA, pp 5998\u20136008"},{"key":"2803_CR17","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Houlsby N (2020) An image is worth 16x16 words: transformers for image recognition at scale. In: International conference on learning representations, 11929"},{"key":"2803_CR18","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Zhou Y (2021) Transunet: transformers make strong encoders for medical image segmentation. CoRR, vol. abs\/2102.04306"},{"key":"2803_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1970392.1970395","volume":"58","author":"EJ Cand\u00e8s","year":"2011","unstructured":"Cand\u00e8s EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58:1\u201337","journal-title":"J ACM"},{"key":"2803_CR20","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/j.neucom.2018.10.012","volume":"323","author":"Y Li","year":"2019","unstructured":"Li Y, Liu G, Liu Q, Sun Y, Chen S (2019) Moving object detection via segmentation and saliency constrained RPCA. Neurocomputing 323:352\u2013362","journal-title":"Neurocomputing"},{"key":"2803_CR21","doi-asserted-by":"publisher","first-page":"41026","DOI":"10.1109\/ACCESS.2020.2977273","volume":"8","author":"Z Hu","year":"2020","unstructured":"Hu Z, Wang Y, Su R, Bian X, Wei H, He G (2020) Moving object detection based on non-convex RPCA with segmentation constraint. IEEE Access 8:41026\u201341036","journal-title":"IEEE Access"},{"key":"2803_CR22","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aacddf","volume":"63","author":"J Zhang","year":"2018","unstructured":"Zhang J, Wang G, Xie H, Zhang S, Shi Z, Gu L (2018) Vesselness-constrained robust PCA for vessel enhancement in x-ray coronary angiograms. Phys Med Biol 63:155019","journal-title":"Phys Med Biol"},{"key":"2803_CR23","first-page":"455","volume-title":"European conference on computer vision (ECCV)","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Jin W, Xu J, Cheng MM (2020) Gradient-induced co-saliency detection. European conference on computer vision (ECCV). Glasgow, UK, August, pp 455\u2013472"},{"key":"2803_CR24","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.carrev.2018.02.012","volume":"19","author":"S Hashimoto","year":"2018","unstructured":"Hashimoto S, Takahashi A, Yamada T, Mizuguchi Y, Taniguchi N, Nakajima S, Hata T (2018) Usefulness of the twin guidewire method during retrieval of the broken tip of a microcatheter entrapped in a heavily calcified coronary artery. Cardiovasc Revasc Med 19:28\u201330","journal-title":"Cardiovasc Revasc Med"},{"key":"2803_CR25","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Rueckert D (2018) Attention u-net: learning where to look for the pancreas. In: International conference on medical imaging with deep learning, Amsterdam, p 03999"},{"key":"2803_CR26","doi-asserted-by":"publisher","first-page":"122798","DOI":"10.1109\/ACCESS.2020.3007719","volume":"8","author":"G Huang","year":"2020","unstructured":"Huang G, Zhu J, Li J, Wang Z, Cheng L, Liu L, Zhou J (2020) Channel-attention U-Net: channel attention mechanism for semantic segmentation of esophagus and esophageal cancer. IEEE Access 8:122798\u2013122810","journal-title":"IEEE Access"},{"key":"2803_CR27","first-page":"1","volume-title":"14th international congress on image and signal processing","author":"G Zhang","year":"2021","unstructured":"Zhang G, Wong HC, Wang C, Zhu J, Lu L, Teng G (2021) A temporary transformer network for guide-wire segmentation. 14th international congress on image and signal processing. BioMedical engineering and informatics. Shanghai, China, pp 1\u20135"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02803-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-022-02803-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-022-02803-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T07:17:31Z","timestamp":1679728651000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-022-02803-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":27,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["2803"],"URL":"https:\/\/doi.org\/10.1007\/s11548-022-02803-z","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]},"assertion":[{"value":"6 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2022","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 the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}