{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:41:50Z","timestamp":1743079310707,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721137"},{"type":"electronic","value":"9783031721144"}],"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\/978-3-031-72114-4_40","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:01:43Z","timestamp":1727874103000},"page":"415-424","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lost in\u00a0Tracking: Uncertainty-Guided Cardiac Cine MRI Segmentation at\u00a0Right Ventricle Base"],"prefix":"10.1007","author":[{"given":"Yidong","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Orlando","family":"Simonetti","sequence":"additional","affiliation":[]},{"given":"Yuchi","family":"Han","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Tao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"40_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1007\/978-3-030-93722-5_27","volume-title":"Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge","author":"TW Arega","year":"2022","unstructured":"Arega, T.W., Legrand, F., Bricq, S., Meriaudeau, F.: Using MRI-specific data augmentation to\u00a0enhance the\u00a0segmentation of\u00a0right ventricle in\u00a0multi-disease, multi-center and\u00a0multi-view cardiac MRI. In: Puyol Ant\u00f3n, E., et al. (eds.) STACOM 2021. LNCS, vol. 13131, pp. 250\u2013258. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-93722-5_27"},{"key":"40_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1007\/978-3-030-00937-3_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"W Bai","year":"2018","unstructured":"Bai, W.: Recurrent neural networks for aortic image sequence segmentation with sparse annotations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018, Part IV. LNCS, vol. 11073, pp. 586\u2013594. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_67"},{"issue":"8","key":"40_CR3","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"40_CR4","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"40_CR5","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","volume":"40","author":"VM Campello","year":"2021","unstructured":"Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &Ms challenge. IEEE Trans. Med. Imaging 40(12), 3543\u20133554 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"40_CR6","unstructured":"Chen, T., Fox, E., Guestrin, C.: Stochastic gradient Hamiltonian Monte Carlo. In: International Conference on Machine Learning, pp. 1683\u20131691. PMLR (2014)"},{"key":"40_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-59719-1_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Dong","year":"2020","unstructured":"Dong, S., et al.: DeU-net: deformable U-net for 3D cardiac MRI video segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part IV. LNCS, vol. 12264, pp. 98\u2013107. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_10"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Farr\u00e9, J., Anderson, R.H., Cabrera, J.A., S\u00e1nchez-Quintana, D., Rubio, J.M., Benezet-Mazuecos, J.: Cardiac anatomy for catheter mapping and ablation of arrhythmias. Catheter Ablation Cardiac Arrhythmias 74\u2013102 (2011)","DOI":"10.1016\/B978-1-4377-1368-8.00006-4"},{"key":"40_CR9","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning [eb\/ol]. arXiv preprint arxiv:1506.02142 (2015)"},{"issue":"2","key":"40_CR10","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.cardfail.2020.10.006","volume":"27","author":"Y Han","year":"2021","unstructured":"Han, Y., et al.: Ranolazine improves right ventricular function in patients with precapillary pulmonary hypertension: results from a double-blind, randomized, placebo-controlled trial. J. Cardiac Fail. 27(2), 253\u2013257 (2021)","journal-title":"J. Cardiac Fail."},{"issue":"suppl 1","key":"40_CR11","doi-asserted-by":"publisher","first-page":"i2","DOI":"10.1136\/hrt.2005.077875","volume":"92","author":"S Ho","year":"2006","unstructured":"Ho, S., Nihoyannopoulos, P.: Anatomy, echocardiography, and normal right ventricular dimensions. Heart 92(suppl 1), i2\u2013i13 (2006)","journal-title":"Heart"},{"issue":"2","key":"40_CR12","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, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"40_CR13","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"40_CR14","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-1-4614-7657-3_19","volume-title":"Intraoperative Imaging and Image-Guided Therapy","author":"R Kikinis","year":"2014","unstructured":"Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277\u2013289. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4614-7657-3_19"},{"key":"40_CR15","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. arxiv e-prints, arXiv preprint arXiv:1612.01474, vol. 5 (2016)"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Mart\u00edn-Isla, C., et\u00a0al.: Deep learning segmentation of the right ventricle in cardiac MRI: the M &Ms challenge. IEEE J. Biomed. Health Inform. (2023)","DOI":"10.1109\/JBHI.2023.3267857"},{"key":"40_CR17","unstructured":"Nilsson, D., Sminchisescu, C.: Semantic video segmentation by gated recurrent flow propagation. arXiv preprint arXiv:1612.08871 (2016)"},{"key":"40_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-00934-2_53","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Qin","year":"2018","unstructured":"Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018, Part II. LNCS, vol. 11071, pp. 472\u2013480. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_53"},{"key":"40_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"11","key":"40_CR20","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1136\/hrt.2007.132779","volume":"94","author":"F Sheehan","year":"2008","unstructured":"Sheehan, F., Redington, A.: The right ventricle: anatomy, physiology and clinical imaging. Heart 94(11), 1510\u20131515 (2008)","journal-title":"Heart"},{"issue":"1","key":"40_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1148\/radiol.2018180513","volume":"290","author":"Q Tao","year":"2019","unstructured":"Tao, Q., et al.: Deep learning-based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study. Radiology 290(1), 81\u201388 (2019)","journal-title":"Radiology"},{"issue":"2","key":"40_CR22","doi-asserted-by":"publisher","first-page":"204589401989977","DOI":"10.1177\/2045894019899778","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., et al.: Diagnostic and prognostic value of right ventricular eccentricity index in pulmonary artery hypertension. Pulm. Circul. 10(2), 2045894019899778 (2020)","journal-title":"Pulm. Circul."},{"key":"40_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-030-66415-2_19","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"P Wu","year":"2020","unstructured":"Wu, P., et al.: Cardiac MR image sequence segmentation with temporal motion encoding. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020, Part I. LNCS, vol. 12535, pp. 298\u2013309. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66415-2_19"},{"key":"40_CR24","doi-asserted-by":"publisher","first-page":"103356","DOI":"10.1016\/j.compbiomed.2019.103356","volume":"111","author":"W Yan","year":"2019","unstructured":"Yan, W., Wang, Y., van der Geest, R.J., Tao, Q.: Cine MRI analysis by deep learning of optical flow: adding the temporal dimension. Comput. Biol. Med. 111, 103356 (2019)","journal-title":"Comput. Biol. Med."},{"key":"40_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-00937-3_70","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"W Yan","year":"2018","unstructured":"Yan, W., Wang, Y., Li, Z., van der Geest, R.J., Tao, Q.: Left ventricle segmentation via optical-flow-net from short-axis cine MRI: preserving the temporal coherence of cardiac motion. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018, Part IV. LNCS, vol. 11073, pp. 613\u2013621. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_70"},{"key":"40_CR26","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1007\/s10278-018-0061-3","volume":"31","author":"P Yilmaz","year":"2018","unstructured":"Yilmaz, P., Wallecan, K., Kristanto, W., Aben, J.P., Moelker, A.: Evaluation of a semi-automatic right ventricle segmentation method on short-axis MR images. J. Digit. Imaging 31, 670\u2013679 (2018)","journal-title":"J. Digit. Imaging"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Simonetti, O., Han, Y., Tao, Q.: Artificial intelligence failure in cardiac magnetic resonance image segmentation: An empirical study. J. Cardiovasc. Magn. Reson. 26 (2024)","DOI":"10.1016\/j.jocmr.2024.100880"},{"key":"40_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: Bayesian uncertainty estimation by Hamiltonian Monte Carlo: applications to cardiac MRI segmentation (2024)","DOI":"10.59275\/j.melba.2024-88fa"},{"key":"40_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1007\/978-3-031-16452-1_51","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022","author":"Y Zhao","year":"2022","unstructured":"Zhao, Y., Yang, C., Schweidtmann, A., Tao, Q.: Efficient Bayesian uncertainty estimation for nnU-net. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13438, pp. 535\u2013544. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16452-1_51"}],"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\/978-3-031-72114-4_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:06:06Z","timestamp":1727874366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721137","9783031721144"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72114-4_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 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 are 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"}}]}}