{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:09:07Z","timestamp":1776816547296,"version":"3.51.2"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T00:00:00Z","timestamp":1685059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2020YFB1711500"],"award-info":[{"award-number":["2020YFB1711500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2020YFB1711501"],"award-info":[{"award-number":["2020YFB1711501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"crossref","award":["2020YFB1711503"],"award-info":[{"award-number":["2020YFB1711503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the general program of National Natural Science Fund of China","award":["81971693"],"award-info":[{"award-number":["81971693"]}]},{"name":"the general program of National Natural Science Fund of China","award":["61971445"],"award-info":[{"award-number":["61971445"]}]},{"name":"the general program of National Natural Science Fund of China","award":["61971089"],"award-info":[{"award-number":["61971089"]}]},{"name":"the funding of Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging"},{"name":"Hainan Province Key Research and Development Plan","award":["ZDYF2021SHFZ244"],"award-info":[{"award-number":["ZDYF2021SHFZ244"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["DUT22YG229"],"award-info":[{"award-number":["DUT22YG229"]}]},{"name":"the funding of Liaoning Key Lab of IC & BME System"},{"name":"Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"DOI":"10.1007\/s11548-023-02931-0","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T15:01:54Z","timestamp":1685113314000},"page":"87-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas"],"prefix":"10.1007","volume":"19","author":[{"given":"Mingrui","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghua","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauri","family":"Kettunen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1813-2162","authenticated-orcid":false,"given":"Hongkai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"2931_CR1","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s10278-017-9976-3","volume":"30","author":"MD Kohli","year":"2017","unstructured":"Kohli MD, Summers RM, Geis JR (2017) Medical image data and datasets in the era of machine learning-whitepaper from the 2016 C-MIMI meeting dataset session. J Digit Imaging 30:392\u2013399. https:\/\/doi.org\/10.1007\/s10278-017-9976-3","journal-title":"J Digit Imaging"},{"key":"2931_CR2","unstructured":"Li X, Yu L, Chen H, Fu CW, Heng PA (2020) Transformation-consistent self-ensembling model for semisupervised medical image segmentation. IEEE Trans Neural Netw Learn Syst pp. 1\u201312"},{"key":"2931_CR3","doi-asserted-by":"crossref","unstructured":"Luo XD, Chen JN, Song T, Wang GT, (2021) Assoc advancement artificial, I. Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the 35th AAAI conference on artificial intelligence\/33rd conference on innovative applications of artificial intelligence\/11th symposium on educational advances in artificial intelligence, Electr Network, Feb 02\u201309, 2021; pp. 8801\u20138809","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"2931_CR4","doi-asserted-by":"crossref","unstructured":"Sedai S, Mahapatra D, Hewavitharanage S, Maetschke S, Garnavi R (2017) Semi-supervised segmentation of optic cup in retinal fundus images using variational autoencoder. In: 20th international conference on medical image computing and computer-assisted intervention, MICCAI 2017, Proceedings. LNCS 10434, pp. 75\u201382","DOI":"10.1007\/978-3-319-66185-8_9"},{"key":"2931_CR5","doi-asserted-by":"crossref","unstructured":"Dai C, Mo Y, Angelini E, Guo Y, Bai W (2019) Transfer learning from partial annotations for whole brain segmentation. In: Proceedings of the domain adaptation and representation transfer and medical image learning with less labels and imperfect data, Cham, 2019, pp. 199\u2013206","DOI":"10.1007\/978-3-030-33391-1_23"},{"key":"2931_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107826","author":"XC Chen","year":"2021","unstructured":"Chen XC, Yao LN, Zhou T, Dong JM, Zhang Y (2021) Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images. Pattern Recogn. https:\/\/doi.org\/10.1016\/j.patcog.2021.107826","journal-title":"Pattern Recogn"},{"key":"2931_CR7","doi-asserted-by":"publisher","first-page":"101551","DOI":"10.1016\/j.media.2019.101551","volume":"58","author":"PA Ganaye","year":"2019","unstructured":"Ganaye PA, Sdika M, Triggs B, Benoit-Cattin H (2019) Removing segmentation inconsistencies with semi-supervised non-adjacency constraint. Med Image Anal 58:101551. https:\/\/doi.org\/10.1016\/j.media.2019.101551","journal-title":"Med Image Anal"},{"key":"2931_CR8","doi-asserted-by":"publisher","first-page":"2795","DOI":"10.1109\/tmi.2020.3047807","volume":"40","author":"L Wang","year":"2021","unstructured":"Wang L, Guo D, Wang GT, Zhang ST (2021) Annotation-efficient learning for medical image segmentation based on noisy pseudo labels and adversarial learning. IEEE Trans Med Imaging 40:2795\u20132807. https:\/\/doi.org\/10.1109\/tmi.2020.3047807","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101840","author":"ZW Zhou","year":"2021","unstructured":"Zhou ZW, Sodha V, Pang JX, Gotway MB, Liang JM (2021) Models genesis. Med Image Anal. https:\/\/doi.org\/10.1016\/j.media.2020.101840","journal-title":"Med Image Anal"},{"key":"2931_CR10","doi-asserted-by":"publisher","first-page":"101746","DOI":"10.1016\/j.media.2020.101746","volume":"64","author":"J Zhu","year":"2020","unstructured":"Zhu J, Li Y, Hu Y, Ma K, Zhou SK, Zheng Y (2020) Rubik\u2019s Cube+: A self-supervised feature learning framework for 3D medical image analysis. Med Image Anal 64:101746. https:\/\/doi.org\/10.1016\/j.media.2020.101746","journal-title":"Med Image Anal"},{"key":"2931_CR11","doi-asserted-by":"crossref","unstructured":"Dong NQ, Kampffmeyer M, Voiculescu I (2021) Self-supervised multi-task representation learning for sequential medical images. in: Proceedings of the European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD), Electr Network, Sep 13\u201317, pp. 779\u2013794","DOI":"10.1007\/978-3-030-86523-8_47"},{"key":"2931_CR12","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1109\/jbhi.2015.2425041","volume":"19","author":"H Chen","year":"2015","unstructured":"Chen H, Ni D, Qin J, Li SL, Yang X, Wang TF, Heng PA (2015) Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inform 19:1627\u20131636. https:\/\/doi.org\/10.1109\/jbhi.2015.2425041","journal-title":"IEEE J Biomed Health Inform"},{"key":"2931_CR13","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/tpami.2015.2437384","volume":"38","author":"R Girshick","year":"2016","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38:142\u2013158. https:\/\/doi.org\/10.1109\/tpami.2015.2437384","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2931_CR14","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1109\/tmi.2020.3045775","volume":"40","author":"HJ Cui","year":"2021","unstructured":"Cui HJ, Wei D, Ma K, Gu S, Zheng YF (2021) A unified framework for generalized low-shot medical image segmentation with scarce data. IEEE Trans Med Imaging 40:2656\u20132671. https:\/\/doi.org\/10.1109\/tmi.2020.3045775","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR15","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1109\/tmi.2020.3043375","volume":"40","author":"YH Lu","year":"2021","unstructured":"Lu YH, Zheng K, Li WJ, Wang YR, Harrison AP, Lin CH, Wang S, Xiao J, Lu L, Kuo CF, Miao S (2021) Contour transformer network for one-shot segmentation of anatomical structures. IEEE Trans Med Imaging 40:2672\u20132684. https:\/\/doi.org\/10.1109\/tmi.2020.3043375","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26216-9","author":"SS Wang","year":"2021","unstructured":"Wang SS, Li C, Wang RP, Liu ZY, Wang MY, Tan HN, Wu YP, Liu XF, Sun H, Yang R, Liu X, Chen J, Zhou HH, Ben AI, Zheng HR (2021) Annotation-efficient deep learning for automatic medical image segmentation. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-021-26216-9","journal-title":"Nat Commun"},{"key":"2931_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neunet.2018.10.009","volume":"109","author":"Y Hu","year":"2019","unstructured":"Hu Y, Soltoggio A, Lock R, Carter S (2019) A fully convolutional two-stream fusion network for interactive image segmentation. Neural Netw 109:31\u201342. https:\/\/doi.org\/10.1016\/j.neunet.2018.10.009","journal-title":"Neural Netw"},{"key":"2931_CR18","doi-asserted-by":"publisher","first-page":"102102","DOI":"10.1016\/j.media.2021.102102","volume":"72","author":"X Luo","year":"2021","unstructured":"Luo X, Wang G, Song T, Zhang J, Aertsen M, Deprest J, Ourselin S, Vercauteren T, Zhang S (2021) MIDeepSeg: minimally interactive segmentation of unseen objects from medical images using deep learning. Med Image Anal 72:102102. https:\/\/doi.org\/10.1016\/j.media.2021.102102","journal-title":"Med Image Anal"},{"key":"2931_CR19","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","volume":"37","author":"G Wang","year":"2018","unstructured":"Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2018) Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging 37:1562\u20131573. https:\/\/doi.org\/10.1109\/TMI.2018.2791721","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR20","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/tpami.2018.2840695","volume":"41","author":"GT Wang","year":"2019","unstructured":"Wang GT, Zuluaga MA, Li WQ, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2019) DeeplGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans Pattern Anal Mach Intell 41:1559\u20131572. https:\/\/doi.org\/10.1109\/tpami.2018.2840695","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2931_CR21","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2016.2621185","volume":"36","author":"M Rajchl","year":"2017","unstructured":"Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, Rueckert D (2017) DeepCut: object segmentation from bounding box annotations using convolutional neural networks. IEEE Trans Med Imaging 36:674\u2013683. https:\/\/doi.org\/10.1109\/TMI.2016.2621185","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR22","first-page":"99","volume-title":"GeoS: Geodesic Image Segmentation","author":"A Criminisi","year":"2008","unstructured":"Criminisi A, Sharp T, Blake A (2008) GeoS: Geodesic Image Segmentation. Springer, Berlin, pp 99\u2013112"},{"key":"2931_CR23","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s11263-008-0191-z","volume":"82","author":"X Bai","year":"2009","unstructured":"Bai X, Sapiro G (2009) Geodesic matting: a framework for fast interactive image and video segmentation and matting. Int J Comput Vision 82:113\u2013132. https:\/\/doi.org\/10.1007\/s11263-008-0191-z","journal-title":"Int J Comput Vision"},{"key":"2931_CR24","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2019","unstructured":"Wang G, Zuluaga MA, Li W, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, Vercauteren T (2019) DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans Pattern Anal Mach Intell 41:1559\u20131572. https:\/\/doi.org\/10.1109\/TPAMI.2018.2840695","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2931_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat Methods"},{"key":"2931_CR26","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp \u00c7, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SMS, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Leemput KV (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993\u20132024. https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans Med Imaging"},{"key":"2931_CR27","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, van Ginneken B, Bilello M, Bilic P, Christ PF, Do RKG, Gollub MJ, Heckers SH, Huisman H, Jarnagin WR, McHugo MK, Napel S, Pernicka JSG, Rhode K, Tobon-Gomez C, Vorontsov E, Meakin JA, Ourselin S, Wiesenfarth M, Arbel\u00e1ez P, Bae B, Chen S, Daza L, Feng J, He B, Isensee F, Ji Y, Jia F, Kim I, Maier-Hein K, Merhof D, Pai A, Park B, Perslev M, Rezaiifar R, Rippel O, Sarasua I, Shen W, Son J, Wachinger C, Wang L, Wang Y, Xia Y, Xu D, Xu Z, Zheng Y, Simpson AL, Maier-Hein L, Cardoso MJ (2022) The medical segmentation Decathlon. Nat Commun 13:4128. https:\/\/doi.org\/10.1038\/s41467-022-30695-9","journal-title":"Nat Commun"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-023-02931-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-023-02931-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-023-02931-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T12:18:41Z","timestamp":1704457121000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-023-02931-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,26]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["2931"],"URL":"https:\/\/doi.org\/10.1007\/s11548-023-02931-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,26]]},"assertion":[{"value":"1 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 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 have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Declarations.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and material"}},{"value":"The source code and trained models are open source ().","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}