{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:34:43Z","timestamp":1743082483872,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031210136"},{"type":"electronic","value":"9783031210143"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21014-3_9","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:43:40Z","timestamp":1671111820000},"page":"81-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Plug-and-Play Shape Refinement Framework for\u00a0Multi-site and\u00a0Lifespan Brain Skull Stripping"],"prefix":"10.1007","author":[{"given":"Yunxiang","family":"Li","sequence":"first","affiliation":[]},{"given":"Ruilong","family":"Dan","sequence":"additional","affiliation":[]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xiangde","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Chenghao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Gangyong","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Huiyu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"You","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yaqi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"David W Shattuck, Stephanie R Sandor-Leahy, Kirt A Schaper, David A Rottenberg, and Richard M Leahy. Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 13(5):856\u2013876, 2001","DOI":"10.1006\/nimg.2000.0730"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Stephen M Smith. Fast robust automated brain extraction. Human brain mapping, 17(3):143\u2013155, 2002","DOI":"10.1002\/hbm.10062"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"\u00d6zg\u00fcn \u00c7i\u00e7ek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and Olaf Ronneberger. 3d u-net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention, pages 424\u2013432. Springer, 2016","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1007\/978-3-030-87196-3_30","volume":"2021","author":"Xiangde Luo","year":"2021","unstructured":"Luo, Xiangde, Liao, Wenjun, Chen, Jieneng, Song, Tao, Chen, Yinan, Zhang, Shichuan, Chen, Nianyong, Wang, Guotai, Zhang, Shaoting: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In Medical Image Computing and Computer Assisted Intervention - MICCAI 2021, 318\u2013329 (2021)","journal-title":"In Medical Image Computing and Computer Assisted Intervention - MICCAI"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Mobarakol Islam, VS Vibashan, V Jose, Navodini Wijethilake, Uppal Utkarsh, and Hongliang Ren. Brain tumor segmentation and survival prediction using 3d attention unet. In International MICCAI Brainlesion Workshop, pages 262\u2013272. Springer, 2019","DOI":"10.1007\/978-3-030-46640-4_25"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Wei Yu, Bin Fang, Yongqing Liu, Mingqi Gao, Shenhai Zheng, and Yi Wang. Liver vessels segmentation based on 3d residual u-net. In 2019 IEEE International Conference on Image Processing (ICIP), pages 250\u2013254. IEEE, 2019","DOI":"10.1109\/ICIP.2019.8802951"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Wenxuan Wang, Chen Chen, Meng Ding, Hong Yu, Sen Zha, and Jiangyun Li. Transbts: Multimodal brain tumor segmentation using transformer. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 109\u2013119. Springer, 2021","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Zhong, Tao, Zhao, Fenqiang, Pei, Yuchen, Ning, Zhenyuan, Liao, Lufan, Zhengwang, Wu., Niu, Yuyu, Wang, Li., Dinggang Shen, Yu., Zhang, et al.: Dika-nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques. NeuroImage 227,(2021)","DOI":"10.1016\/j.neuroimage.2020.117649"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Qian Zhang, Li Wang, Xiaopeng Zong, Weili Lin, Gang Li, and Dinggang Shen. Frnet: Flattened residual network for infant mri skull stripping. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pages 999\u20131002. IEEE, 2019","DOI":"10.1109\/ISBI.2019.8759167"},{"key":"9_CR10","unstructured":"Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, et al. Dispensed transformer network for unsupervised domain adaptation. arXiv preprint arXiv:2110.14944, 2021"},{"key":"9_CR11","doi-asserted-by":"publisher","first-page":"99065","DOI":"10.1109\/ACCESS.2019.2929258","volume":"7","author":"Qi Dou","year":"2019","unstructured":"Dou, Qi., Ouyang, Cheng, Chen, Cheng, Chen, Hao, Glocker, Ben, Zhuang, Xiahai, Heng, Pheng-Ann.: Pnp-adanet: Plug-and-play adversarial domain adaptation network at unpaired cross-modality cardiac segmentation. IEEE Access 7, 99065\u201399076 (2019)","journal-title":"IEEE Access"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Mark S. Nixon and Alberto S. Aguado. Chapter 7 - object description. In Mark S. Nixon and Alberto S. Aguado, editors, Feature Extraction & Image Processing for Computer Vision (Third Edition), pages 343\u2013397. Academic Press, Oxford, third edition edition, 2012","DOI":"10.1016\/B978-0-12-396549-3.00007-0"},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1687-6180-2013-161","volume":"2013","author":"Christoph Dalitz","year":"2013","unstructured":"Dalitz, Christoph, Brandt, Christian, Goebbels, Steffen, Kolanus, David: Fourier descriptors for broken shapes. EURASIP Journal on Advances in Signal Processing 2013(1), 1\u201311 (2013)","journal-title":"EURASIP Journal on Advances in Signal Processing"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE\/CVF International Conference on Computer Vision, pages 10012\u201310022, 2021","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"9_CR15","unstructured":"Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qun Jin, Lingling Sun, Qianni Zhang, Qisi Lian, Guiping Qian, Neng Xia, et al. Agmb-transformer: Anatomy-guided multi-branch transformer network for automated evaluation of root canal therapy. IEEE Journal of Biomedical and Health Informatics, 2021"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, and Yaqi Wang. Gt u-net: A u-net like group transformer network for tooth root segmentation. In International Workshop on Machine Learning in Medical Imaging, pages 386\u2013395. Springer, 2021","DOI":"10.1007\/978-3-030-87589-3_40"},{"key":"9_CR17","unstructured":"Zilong Huang, Youcheng Ben, Guozhong Luo, Pei Cheng, Gang Yu, and Bin Fu. Shuffle transformer: Rethinking spatial shuffle for vision transformer. arXiv preprint arXiv:2106.03650, 2021"},{"key":"9_CR18","unstructured":"Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jon Shlens. Stand-alone self-attention in vision models. Advances in Neural Information Processing Systems, 32, 2019"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V. Le. Attention augmented convolutional networks. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), October 2019","DOI":"10.1109\/ICCV.2019.00338"},{"key":"9_CR20","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Simon F Eskildsen, Pierrick Coup\u00e9, Vladimir Fonov, Jos\u00e9 V Manj\u00f3n, Kelvin K Leung, Nicolas Guizard, Shafik N Wassef, Lasse Riis \u00d8stergaard, D Louis Collins, Alzheimer\u2019s Disease Neuroimaging Initiative, et al. Beast: brain extraction based on nonlocal segmentation technique. NeuroImage, 59(3):2362\u20132373, 2012","DOI":"10.1016\/j.neuroimage.2011.09.012"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Clifford R Jack Jr, Matt A Bernstein, Nick C Fox, Paul Thompson, Gene Alexander, Danielle Harvey, Bret Borowski, Paula J Britson, Jennifer L. Whitwell, Chadwick Ward, et al. The alzheimer\u2019s disease neuroimaging initiative (adni): Mri methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685\u2013691, 2008","DOI":"10.1002\/jmri.21049"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Antonios Makropoulos, Emma C Robinson, Andreas Schuh, Robert Wright, Sean Fitzgibbon, Jelena Bozek, Serena J Counsell, Johannes Steinweg, Katy Vecchiato, Jonathan Passerat-Palmbach, et al. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage, 173:88\u2013112, 2018","DOI":"10.1016\/j.neuroimage.2018.01.054"},{"key":"9_CR24","unstructured":"Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223\u20132232, 2017"},{"issue":"5","key":"9_CR25","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1109\/TMI.2021.3055428","volume":"40","author":"Yue Sun","year":"2021","unstructured":"Sun, Yue, Gao, Kun, Zhengwang, Wu., Li, Guannan, Zong, Xiaopeng, Lei, Zhihao, Wei, Ying, Ma, Jun, Yang, Xiaoping, Feng, Xue, et al.: Multi-site infant brain segmentation algorithms: The iseg-2019 challenge. IEEE Transactions on Medical Imaging 40(5), 1363\u20131376 (2021)","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"9_CR26","unstructured":"Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell. Tent: Fully test-time adaptation by entropy minimization. In International Conference on Learning Representations, 2021"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21014-3_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:45:23Z","timestamp":1671111923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21014-3_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031210136","9783031210143"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21014-3_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}