{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T10:53:50Z","timestamp":1779101630090,"version":"3.51.4"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_20","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"209-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Dynamic Position Transformation and\u00a0Boundary Refinement Network for\u00a0Left Atrial Segmentation"],"prefix":"10.1007","author":[{"given":"Fangqiang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxuan","family":"Tu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malitha","family":"Gunawardhana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jichao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"issue":"8","key":"20_CR1","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1016\/j.jacc.2007.09.064","volume":"51","author":"B Burstein","year":"2008","unstructured":"Burstein, B., Nattel, S.: Atrial fibrosis: mechanisms and clinical relevance in atrial fibrillation. J. Am. Coll. Cardiol. 51(8), 802\u2013809 (2008)","journal-title":"J. Am. Coll. Cardiol."},{"key":"20_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/978-3-030-12029-0_32","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"C Chen","year":"2019","unstructured":"Chen, C., Bai, W., Rueckert, D.: Multi-task learning for left atrial segmentation on GE-MRI. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 292\u2013301. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_32"},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.1093\/eurheartj\/ehv233","volume":"36","author":"BJ Hansen","year":"2015","unstructured":"Hansen, B.J., et al.: Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts. Eur. Heart J. 36, 2390\u20132401 (2015)","journal-title":"Eur. Heart J."},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Jiang, B., Xu, F., Tu, W., Yang, C.: Channel-wise attention in 3d convolutional networks for violence detection. In: 2019 International Conference on Intelligent Computing and its Emerging Applications (ICEA), pp. 59\u201364. IEEE (2019)","DOI":"10.1109\/ICEA.2019.8858306"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, B., Xu, F., Xia, J., Yang, C., Huang, W., Huang, Y.: Stacked multi-scale attention network for image colorization. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2225\u20132229. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746133"},{"key":"20_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/978-3-030-59710-8_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Li","year":"2020","unstructured":"Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 552\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_54"},{"issue":"7","key":"20_CR7","doi-asserted-by":"publisher","first-page":"4554","DOI":"10.1002\/mp.15670","volume":"49","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Wang, W., Luo, G., Wang, K., Liang, D., Li, S.: Uncertainty-guided symmetric multilevel supervision network for 3d left atrium segmentation in late gadolinium-enhanced mri. Med. Phys. 49(7), 4554\u20134565 (2022)","journal-title":"Med. Phys."},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Louren\u00e7o, A., et al.: Left atrial ejection fraction estimation using seganet for fully automated segmentation of cine mri. In: Statistical Atlases and Computational Models of the Heart, pp. 137\u2013145. Springer (2021)","DOI":"10.1007\/978-3-030-68107-4_14"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: Attribute-missing graph clustering network. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 15392\u201315401 (2024)","DOI":"10.1609\/aaai.v38i14.29464"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Tu, W., Xiao, B., Liu, X., Zhou, S., Cai, Z., Cheng, J.: Revisiting initializing then refining: an incomplete and missing graph imputation network. IEEE Trans. Neural Networks Learn. Syst., 1\u201314 (2024)","DOI":"10.1109\/TNNLS.2024.3349850"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Tu, W., Zhou, S., Liu, X., Ge, C., Cai, Z., Liu, Y.: Hierarchically contrastive hard sample mining for graph self-supervised pretraining. IEEE Trans. Neural Networks Learn. Syst., 1\u201314 (2023)","DOI":"10.1109\/TNNLS.2023.3297607"},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TMI.2021.3117495","volume":"41","author":"F Uslu","year":"2021","unstructured":"Uslu, F., Varela, M., Boniface, G., Mahenthran, T., Chubb, H., Bharath, A.A.: La-net: a multi-task deep network for the segmentation of the left atrium. IEEE Trans. Med. Imaging 41(2), 456\u2013464 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-030-12029-0_35","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"S Vesal","year":"2019","unstructured":"Vesal, S., Ravikumar, N., Maier, A.: Dilated convolutions in neural networks for left atrial segmentation in 3D gadolinium enhanced-MRI. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 319\u2013328. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_35"},{"key":"20_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-030-87196-3_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency\u00a0training. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 297\u2013306. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_28"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Xia, J., Tan, G., Xiao, Y., Xu, F., Leung, C.S.: Edge-aware multi-scale progressive colorization. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1655\u20131659. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414764"},{"key":"20_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-030-12029-0_23","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"Q Xia","year":"2019","unstructured":"Xia, Q., Yao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 211\u2013220. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_23"},{"issue":"2","key":"20_CR20","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1109\/TMI.2018.2866845","volume":"38","author":"Z Xiong","year":"2018","unstructured":"Xiong, Z., Fedorov, V.V., Fu, X., Cheng, E., Macleod, R., Zhao, J.: Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans. Med. Imaging 38(2), 515\u2013524 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"20_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-39074-7_7","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges","author":"Z Xiong","year":"2020","unstructured":"Xiong, Z., Nalar, A., Jamart, K., Stiles, M.K., Fedorov, V.V., Zhao, J.: Fully automatic 3D bi-atria segmentation from late gadolinium-enhanced mris using double convolutional neural networks. In: Pop, M., et al. (eds.) STACOM 2019. LNCS, vol. 12009, pp. 63\u201371. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39074-7_7"},{"key":"20_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832","volume":"67","author":"Z Xiong","year":"2021","unstructured":"Xiong, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)","journal-title":"Med. Image Anal."},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: Combating uncertainty with novel losses for automatic left atrium segmentation. In: Statistical Atlases and Computational Models of the Heart, pp. 246\u2013254. Springer (2019)","DOI":"10.1007\/978-3-030-12029-0_27"},{"key":"20_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Yu","year":"2019","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.-W., Heng, P.-A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"},{"key":"20_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119105","volume":"214","author":"C Zhao","year":"2023","unstructured":"Zhao, C., et al.: Context-aware network fusing transformer and v-net for semi-supervised segmentation of 3d left atrium. Expert Syst. Appl. 214, 119105 (2023)","journal-title":"Expert Syst. Appl."},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, J., et al.: An image-based model of atrial muscular architecture: effects of structural anisotropy on electrical activation. Circulation: Arrhythmia Electrophysiology 5(2), 361\u2013370 (2012)","DOI":"10.1161\/CIRCEP.111.967950"},{"issue":"8","key":"20_CR27","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.117.005922","volume":"6","author":"J Zhao","year":"2017","unstructured":"Zhao, J., et al.: Three-dimensional integrated functional, structural, and computational mapping to define the structural \u201cfingerprints\u2019\u2019 of heart-specific atrial fibrillation drivers in human heart ex vivo. J. Am. Heart Assoc. 6(8), e005922 (2017)","journal-title":"J. Am. Heart Assoc."},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Puybareau, E., Boutry, N., G\u00e9raud, T.: Do not treat boundaries and regions differently: an example on heart left atrial segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 7447\u20137453. IEEE (2021)","DOI":"10.1109\/ICPR48806.2021.9412755"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:03:32Z","timestamp":1728162212000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}