{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:31:35Z","timestamp":1742981495245,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439865"},{"type":"electronic","value":"9783031439872"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43987-2_21","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"213-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wall Thickness Estimation from\u00a0Short Axis Ultrasound Images via\u00a0Temporal Compatible Deformation Learning"],"prefix":"10.1007","author":[{"given":"Ang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Guijuan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Jialan","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Xiaohua","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Sheng","sequence":"additional","affiliation":[]},{"given":"Yingqi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yumei","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Yingying","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wufeng","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"159755","DOI":"10.1109\/ACCESS.2021.3122256","volume":"9","author":"A Amer","year":"2021","unstructured":"Amer, A., Ye, X., Janan, F.: ResDUnet: a deep learning-based left ventricle segmentation method for echocardiography. IEEE Access 9, 159755\u2013159763 (2021)","journal-title":"IEEE Access"},{"issue":"2","key":"21_CR2","volume":"16","author":"L Chen","year":"2023","unstructured":"Chen, L., Su, Y., Yang, X., Li, C., Yu, J.: Clinical study on LVO-based evaluation of left ventricular wall thickness and volume of AHCM patients. J. Radiat. Res. Appl. Sci. 16(2), 100545 (2023)","journal-title":"J. Radiat. Res. Appl. Sci."},{"key":"21_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-87583-1_7","volume-title":"Simplifying Medical Ultrasound","author":"K Deng","year":"2021","unstructured":"Deng, K., et al.: TransBridge: a lightweight transformer for left ventricle segmentation in\u00a0echocardiography. In: Noble, J.A., Aylward, S., Grimwood, A., Min, Z., Lee, S.-L., Hu, Y. (eds.) ASMUS 2021. LNCS, vol. 12967, pp. 63\u201372. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87583-1_7"},{"issue":"3","key":"21_CR4","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/JBHI.2018.2879188","volume":"23","author":"X Du","year":"2018","unstructured":"Du, X., Tang, R., Yin, S., Zhang, Y., Li, S.: Direct segmentation-based full quantification for left ventricle via deep multi-task regression learning network. IEEE J. Biomed. Health Inf. 23(3), 942\u2013948 (2018)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"21_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101554","volume":"58","author":"R Ge","year":"2019","unstructured":"Ge, R., et al.: PV-LVNet: direct left ventricle multitype indices estimation from 2d echocardiograms of paired apical views with deep neural networks. Med. Image Anal. 58, 101554 (2019)","journal-title":"Med. Image Anal."},{"issue":"15","key":"21_CR6","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1016\/j.jacc.2009.04.094","volume":"54","author":"TD Karamitsos","year":"2009","unstructured":"Karamitsos, T.D., Francis, J.M., Myerson, S., Selvanayagam, J.B., Neubauer, S.: The role of cardiovascular magnetic resonance imaging in heart failure. J. Am. Coll. Cardiol. 54(15), 1407\u20131424 (2009)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"12","key":"21_CR7","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1109\/TUFFC.2020.3003403","volume":"67","author":"S Leclerc","year":"2020","unstructured":"Leclerc, S., et al.: LU-Net: a multistage attention network to improve the robustness of segmentation of left ventricular structures in 2-D echocardiography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67(12), 2519\u20132530 (2020)","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"issue":"11","key":"21_CR8","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1109\/TMI.2019.2905990","volume":"38","author":"MCH Lee","year":"2019","unstructured":"Lee, M.C.H., Petersen, K., Pawlowski, N., Glocker, B., Schaap, M.: TeTrIS: template transformer networks for image segmentation with shape priors. IEEE Trans. Med. Imaging 38(11), 2596\u20132606 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"21_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102461","volume":"79","author":"J Liang","year":"2022","unstructured":"Liang, J., et al.: Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis. Med. Image Anal. 79, 102461 (2022)","journal-title":"Med. Image Anal."},{"key":"21_CR10","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":"4","key":"21_CR11","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"21_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/978-3-030-59713-9_60","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"H Wei","year":"2020","unstructured":"Wei, H., Cao, H., Cao, Y., Zhou, Y., Xue, W., Ni, D., Li, S.: Temporal-consistent segmentation of echocardiography with co-learning from appearance and shape. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 623\u2013632. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_60"},{"key":"21_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102686","volume":"84","author":"H Wei","year":"2023","unstructured":"Wei, H., Ma, J., Zhou, Y., Xue, W., Ni, D.: Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences. Med. Image Anal. 84, 102686 (2023)","journal-title":"Med. Image Anal."},{"issue":"12","key":"21_CR14","doi-asserted-by":"publisher","first-page":"6105","DOI":"10.1109\/JBHI.2022.3221429","volume":"26","author":"W Xue","year":"2022","unstructured":"Xue, W., Cao, H., Ma, J., Bai, T., Wang, T., Ni, D.: Improved segmentation of echocardiography with orientation-congruency of optical flow and motion-enhanced segmentation. IEEE J. Biomed. Health Inf. 26(12), 6105\u20136115 (2022)","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"10","key":"21_CR15","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1109\/TMI.2017.2709251","volume":"36","author":"W Xue","year":"2017","unstructured":"Xue, W., Islam, A., Bhaduri, M., Li, S.: Direct multitype cardiac indices estimation via joint representation and regression learning. IEEE Trans. Med. Imaging 36(10), 2057\u20132067 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"21_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1007\/978-3-319-59050-9_40","volume-title":"Information Processing in Medical Imaging","author":"W Xue","year":"2017","unstructured":"Xue, W., Nachum, I.B., Pandey, S., Warrington, J., Leung, S., Li, S.: Direct estimation of regional wall thicknesses via residual recurrent neural network. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 505\u2013516. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_40"},{"key":"21_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2022.106855","volume":"127","author":"Y Zeng","year":"2023","unstructured":"Zeng, Y., et al.: MAEF-Net: multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography. Ultrasonics 127, 106855 (2023)","journal-title":"Ultrasonics"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43987-2_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:30:22Z","timestamp":1710171022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43987-2_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439865","9783031439872"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43987-2_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","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":"2250","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":"730","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":"32% - 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":"3","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":"5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}