{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:20:41Z","timestamp":1741666841203,"version":"3.38.0"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2022"],"award-info":[{"award-number":["U22A2022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Applied Basic Research Foundation of Liaoning Province of China","award":["2022020421-JH2\/1013","2022JH4\/10100060"],"award-info":[{"award-number":["2022020421-JH2\/1013","2022JH4\/10100060"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11517-024-03195-9","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T12:02:39Z","timestamp":1729512159000},"page":"609-627","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation"],"prefix":"10.1007","volume":"63","author":[{"given":"Sizhe","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Qi","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-1273","authenticated-orcid":false,"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yuliang","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhiqing","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"3195_CR1","doi-asserted-by":"crossref","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11)","DOI":"10.1007\/s10916-018-1088-1"},{"issue":"1","key":"3195_CR2","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S Asgari Taghanaki","year":"2021","unstructured":"Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54(1):137\u2013178. https:\/\/doi.org\/10.1007\/s10462-020-09854-1","journal-title":"Artif Intell Rev"},{"issue":"2","key":"3195_CR3","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/42.993126","volume":"21","author":"S Aylward","year":"2002","unstructured":"Aylward S, Bullitt E (2002) Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging 21(2):61\u201375","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR4","doi-asserted-by":"crossref","unstructured":"Bian X, Luo X, Wang C, Liu W, Lin X (2022) DDA-Net: unsupervised cross-modality medical image segmentation via dual domain adaptation. Comput Meth Programs Biomed 213","DOI":"10.1016\/j.cmpb.2021.106531"},{"issue":"7","key":"3195_CR5","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","volume":"39","author":"C Chen","year":"2020","unstructured":"Chen C, Dou Q, Chen H, Qin J, Heng PA (2020) Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation. IEEE Trans Med Imaging 39(7):2494\u20132505. https:\/\/doi.org\/10.1109\/TMI.2020.2972701","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"3195_CR6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TMI.2022.3184675","volume":"42","author":"C Chen","year":"2023","unstructured":"Chen C, Zhou K, Wang Z, Xiao R (2023) Generative consistency for semi-supervised cerebrovascular segmentation from TOF-MRA. IEEE Trans Med Imaging 42(2):346\u2013353. https:\/\/doi.org\/10.1109\/TMI.2022.3184675","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR7","doi-asserted-by":"publisher","unstructured":"Chen C, Zhou K, Wang Z, Zhang Q, Xiao R (2023) All answers are in the images: a review of deep learning for cerebrovascular segmentation. Comput Med Imaging Graph 107. https:\/\/doi.org\/10.1016\/j.compmedimag.2023.102229","DOI":"10.1016\/j.compmedimag.2023.102229"},{"issue":"3","key":"3195_CR8","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TMI.2021.3117996","volume":"41","author":"J Chen","year":"2022","unstructured":"Chen J, Zhang Z, Xie X, Li Y, Xu T, Ma K, Zheng Y (2022) Beyond mutual information: generative adversarial network for domain adaptation using information bottleneck constraint. IEEE Trans Med Imaging 41(3):595\u2013607. https:\/\/doi.org\/10.1109\/TMI.2021.3117996","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"3195_CR9","doi-asserted-by":"publisher","first-page":"2180","DOI":"10.1161\/STROKEAHA.116.013617","volume":"47","author":"S Devasagayam","year":"2016","unstructured":"Devasagayam S, Wyatt B, Leyden J, Kleinig T (2016) Cerebral venous sinus thrombosis incidence is higher than previously thought a retrospective population-based study. Stroke 47(9):2180\u20132182. https:\/\/doi.org\/10.1161\/STROKEAHA.116.013617","journal-title":"Stroke"},{"key":"3195_CR10","doi-asserted-by":"crossref","unstructured":"Dou Q, Ouyang C, Chen C, Chen H, Heng PA (2018) Unsupervised cross-modality domain adaptation of ConvNets for biomedical image segmentations with adversarial loss. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp 691\u2013697","DOI":"10.24963\/ijcai.2018\/96"},{"key":"3195_CR11","doi-asserted-by":"publisher","first-page":"99065","DOI":"10.1109\/ACCESS.2019.2929258","volume":"7","author":"Q Dou","year":"2019","unstructured":"Dou Q, Ouyang C, Chen C, Chen H, Glocker B, Zhuang X, Heng PA (2019) PnP-AdaNet: plug-and-play adversarial domain adaptation network at unpaired cross-Modality cardiac segmentation. IEEE Access 7:99065\u201399076. https:\/\/doi.org\/10.1109\/ACCESS.2019.2929258","journal-title":"IEEE Access"},{"issue":"1","key":"3195_CR12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/JBHI.2021.3126874","volume":"26","author":"X Du","year":"2022","unstructured":"Du X, Liu Y (2022) Constraint-based unsupervised domain adaptation network for multi-modality cardiac image segmentation. IEEE J Biomed Health Inform 26(1):67\u201378. https:\/\/doi.org\/10.1109\/JBHI.2021.3126874","journal-title":"IEEE J Biomed Health Inform"},{"key":"3195_CR13","unstructured":"Galati F, Falcetta D, Cortese R, Casolla B, Prados F, Burgos N, Zuluaga MA (2023) A2v: a semi-supervised domain adaptation framework for brain vessel segmentation via two-phase training angiography-to-venography translation. In: BMVC 2023, 34th British machine vision conference"},{"key":"3195_CR14","unstructured":"Ganin Y, Lempitsky V (2015) Unsupervised domain adaptation by backpropagation. In: Proceedings of the International Conference on Machine Learning, vol 37, pp 1180\u20131189"},{"key":"3195_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105998","volume":"202","author":"X Guo","year":"2021","unstructured":"Guo X, Xiao R, Lu Y, Chen C, Yan F, Zhou K, He W, Wang Z (2021) Cerebrovascular segmentation from TOF-MRA based on multiple-U-net with focal loss function. Comput Meth Programs Biomed 202:105998. https:\/\/doi.org\/10.1016\/j.cmpb.2021.105998","journal-title":"Comput Meth Programs Biomed"},{"issue":"1","key":"3195_CR16","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/TMI.2021.3105046","volume":"41","author":"X Han","year":"2022","unstructured":"Han X, Qi L, Yu Q, Zhou Z, Zheng Y, Shi Y, Gao Y (2022) Deep symmetric adaptation network for cross-modality medical image segmentation. IEEE Trans Med Imaging 41(1):121\u2013132. https:\/\/doi.org\/10.1109\/TMI.2021.3105046","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR17","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings on IEEE international conference computer vision, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"key":"3195_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings on the IEEE conference computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"3195_CR19","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s11517-022-02723-9","volume":"61","author":"Z Hong","year":"2023","unstructured":"Hong Z, Chen M, Hu W, Yan S, Qu A, Chen L, Chen J (2023) Dual encoder network with transformer-CNN for multi-organ segmentation. Med Biol Eng Comput 61(3):661\u2013671. https:\/\/doi.org\/10.1007\/s11517-022-02723-9","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"3195_CR20","doi-asserted-by":"publisher","first-page":"1016","DOI":"10.1109\/TMI.2018.2876633","volume":"38","author":"Y Huo","year":"2019","unstructured":"Huo Y, Xu Z, Moon H, Bao S, Assad A, Moyo TK, Savona MR, Abramson RG, Landman BA (2019) SynSeg-Net: synthetic segmentation without target modality ground truth. IEEE Trans Med Imaging 38(4):1016\u20131025. https:\/\/doi.org\/10.1109\/TMI.2018.2876633","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR21","doi-asserted-by":"publisher","unstructured":"Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and superresolution. In: Proceedings on European conference computer vision, pp 694\u2013711. https:\/\/doi.org\/10.1007\/978-3-319-46475-6-43","DOI":"10.1007\/978-3-319-46475-6-43"},{"issue":"1","key":"3195_CR22","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0262-8856(83)90006-9","volume":"1","author":"J Kittler","year":"1983","unstructured":"Kittler J (1983) On the accuracy of the Sobel edge detector. Image Vis Comput 1(1):37\u201342. https:\/\/doi.org\/10.1016\/0262-8856(83)90006-9","journal-title":"Image Vis Comput"},{"issue":"1","key":"3195_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/JBHI.2021.3085770","volume":"26","author":"H Lei","year":"2022","unstructured":"Lei H, Liu W, Xie H, Zhao B, Yue G, Lei B (2022) Unsupervised domain adaptation based image synthesis and feature alignment for joint optic disc and cup segmentation. IEEE J Biomed Health Inform 26(1):90\u2013102. https:\/\/doi.org\/10.1109\/JBHI.2021.3085770","journal-title":"IEEE J Biomed Health Inform"},{"key":"3195_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-023-02833-y","author":"D Li","year":"2023","unstructured":"Li D, Peng Y, Sun J, Guo Y (2023) Unsupervised deep consistency learning adaptation network for cardiac cross-modality structural segmentation. Med Biol Eng Comput. https:\/\/doi.org\/10.1007\/s11517-023-02833-y","journal-title":"Med Biol Eng Comput"},{"key":"3195_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Zhang Y, Cui W, Lei B, Kuang X, Zhang T (2022) Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation. IEEE Trans Med Imaging 41(8):1975\u20131989","DOI":"10.1109\/TMI.2022.3151666"},{"key":"3195_CR26","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sanchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"key":"3195_CR27","doi-asserted-by":"publisher","unstructured":"Liu H, Zhuang Y, Song E, Xu X, Hung CC (2022) A bidirectional multilayer contrastive adaptation network with anatomical structure preservation for unpaired cross-modality medical image segmentation. Comput Biol Med 149. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105964","DOI":"10.1016\/j.compbiomed.2022.105964"},{"key":"3195_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen H, Chen Y, Yin W, Shen C (2021) Generic perceptual loss for modeling structured output dependencies. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 5420\u20135428","DOI":"10.1109\/CVPR46437.2021.00538"},{"issue":"12","key":"3195_CR29","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","volume":"37","author":"F Mahmood","year":"2018","unstructured":"Mahmood F, Chen R, Durr NJ (2018) Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans Med Imaging 37(12):2572\u20132581. https:\/\/doi.org\/10.1109\/TMI.2018.2842767","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR30","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: Proceedings on international conference medical image computing and computer- assisted intervention, vol 11764, pp 721\u2013730","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"3195_CR31","doi-asserted-by":"crossref","unstructured":"Mou L, Zhao Y, Fu H, Liu Y, Cheng J, Zheng Y, Su P, Yang J, Chen L, Frangi AF, Akiba M, Liu J (2021) CS2-Net: deep learning segmentation of curvilinear structures in medical imaging. Med Image Anal 67","DOI":"10.1016\/j.media.2020.101874"},{"key":"3195_CR32","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) OFFeNET: an optimally fused fully end-to-end network for automatic dense volumetric 3D intracranial blood vessels segmentation. IEEE Trans Image Process 29:7192\u20137202. https:\/\/doi.org\/10.1109\/TIP.2020.2999854","journal-title":"IEEE Trans Image Process"},{"issue":"10","key":"3195_CR33","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359. https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3195_CR34","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.inffus.2022.09.031","volume":"90","author":"I Qureshi","year":"2023","unstructured":"Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P (2023) Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inf Fusion 90:316\u2013352. https:\/\/doi.org\/10.1016\/j.inffus.2022.09.031","journal-title":"Inf Fusion"},{"key":"3195_CR35","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Proceedings on International Conference Medical Image Computing and Computer- Assisted Intervention, vol pt.III, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"3195_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"3195_CR37","doi-asserted-by":"crossref","unstructured":"Shen W, Peng Z, Wang X, Wang H, Cen J, Jiang D, Xie L, Yang X, Tian Q (2023) A survey on label-efficient deep image segmentation: bridging the gap between weak supervision and dense prediction. IEEE Trans Pattern Anal Mach Intell, pp 1\u201320","DOI":"10.1109\/TPAMI.2023.3246102"},{"key":"3195_CR38","doi-asserted-by":"crossref","unstructured":"Shit S, Paetzold JC, Sekuboyina A, Ezhov I, Unger A, Zhylka A, Pluim JP, Bauer U, Menze BH (2021) cldice-a novel topology-preserving loss function for tubular structure segmentation. In: Proceedings on IEEE Conference Computer Vision and Pattern Recognition, pp 16560\u201316569","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"3195_CR39","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings on international conference learning representations"},{"key":"3195_CR40","doi-asserted-by":"publisher","unstructured":"Tajbakhsh N, Jeyaseelan L, Li Q, Chiang J, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63. https:\/\/doi.org\/10.1016\/j.media.2020.101693","DOI":"10.1016\/j.media.2020.101693"},{"key":"3195_CR41","doi-asserted-by":"crossref","unstructured":"Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 1521\u20131528","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"3195_CR42","doi-asserted-by":"publisher","unstructured":"Tsai YH, Hung WC, Schulter S, Sohn K, Yang MH, Chandraker M (2018) Learning to adapt structured output space for semantic segmentation. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 7472\u20137481. https:\/\/doi.org\/10.1109\/CVPR.2018.00780","DOI":"10.1109\/CVPR.2018.00780"},{"key":"3195_CR43","doi-asserted-by":"crossref","unstructured":"Tumanyan N, Bar-Tal O, Bagon S, Dekel T (2022) Splicing vit features for semantic appearance transfer. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 10748\u201310757","DOI":"10.1109\/CVPR52688.2022.01048"},{"key":"3195_CR44","doi-asserted-by":"publisher","unstructured":"Tzeng E, Hoffman J, Saenko K, Darrell T (2017) Adversarial discriminative domain adaptation. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 2962\u20132971. https:\/\/doi.org\/10.1109\/CVPR.2017.316","DOI":"10.1109\/CVPR.2017.316"},{"key":"3195_CR45","doi-asserted-by":"publisher","unstructured":"Wang TC, Liu MY, Zhu JY, Tao A, Kautz J, Catanzaro B (2018) High-resolution image synthesis and semantic manipulation with conditional GANs. In: Proceedings on IEEE conference computer vision and pattern recognition, pp 8798\u20138807. https:\/\/doi.org\/10.1109\/CVPR.2018.00917","DOI":"10.1109\/CVPR.2018.00917"},{"key":"3195_CR46","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.freeradbiomed.2021.10.019","volume":"177","author":"LY Wu","year":"2021","unstructured":"Wu LY, Cheah IK, Chong JR, Chai YL, Tan JY, Hilal S, Vrooman H, Chen CP, Halliwell B, Lai MKP (2021) Low plasma ergothioneine levels are associated with neurodegeneration and cerebrovascular disease in dementia. Free Radic Biol Med 177:201\u2013211. https:\/\/doi.org\/10.1016\/j.freeradbiomed.2021.10.019","journal-title":"Free Radic Biol Med"},{"key":"3195_CR47","doi-asserted-by":"crossref","unstructured":"Xia L, Zhang H, Wu Y, Song R, Ma Y, Mou L, Liu J, Xie Y, Ma M, Zhao Y (2022) 3D vessel-like structure segmentation in medical images by an edge-reinforced network. Med Image Anal 82","DOI":"10.1016\/j.media.2022.102581"},{"key":"3195_CR48","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? In: Proceedings on advance neural information processing system, vol 4, pp 3320\u20133328"},{"issue":"3","key":"3195_CR49","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116\u20131128","journal-title":"Neuroimage"},{"key":"3195_CR50","doi-asserted-by":"crossref","unstructured":"Zeng G, Lerch TD, Schmaranzer F, Zheng G, Burger J, Gerber K, Tannast M, Siebenrock K, Gerber N (2021) Semantic consistent unsupervised domain adaptation for cross-modality medical image segmentation. In: Proceedings on international conference medical image computing and computer- assisted intervention, vol 12903, pp 201\u2013210","DOI":"10.1007\/978-3-030-87199-4_19"},{"issue":"5","key":"3195_CR51","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"3195_CR52","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1109\/TMI.2018.2854886","volume":"38","author":"H Zhao","year":"2019","unstructured":"Zhao H, Li H, Maurer-Stroh S, Guo Y, Deng Q, Cheng L (2019) Supervised segmentation of unannotated retinal fundus images by synthesis. IEEE Trans Med Imaging 38(1):46\u201356. https:\/\/doi.org\/10.1109\/TMI.2018.2854886","journal-title":"IEEE Trans Med Imaging"},{"key":"3195_CR53","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings on IEEE international conference computer vision, pp 2242\u20132251. https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03195-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03195-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03195-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T15:15:23Z","timestamp":1741619723000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03195-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3195"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03195-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2024","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"}}]}}