{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:17:23Z","timestamp":1769847443568,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030875824","type":"print"},{"value":"9783030875831","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87583-1_7","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T11:37:12Z","timestamp":1632310632000},"page":"63-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["TransBridge: A Lightweight Transformer for Left Ventricle Segmentation in\u00a0Echocardiography"],"prefix":"10.1007","author":[{"given":"Kaizhong","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanda","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Bridge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaochun","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Lip","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yitian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"7_CR1","unstructured":"Chen, J., et al.: TransUNet: Transformers make strong encoders for medical image segmentation, February 2021"},{"key":"7_CR2","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017)"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Chen, X., Williams, B.M., Vallabhaneni, S.R., Czanner, G., Williams, R., Zheng, Y.: Learning active contour models for medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11632\u201311640 (2019)","DOI":"10.1109\/CVPR.2019.01190"},{"key":"7_CR4","unstructured":"Dosovitskiy, A., et al.: An image is worth 16 $$\\times $$ 16 words: transformers for image recognition at scale, October 2020"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"7_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"issue":"2","key":"7_CR7","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.media.2013.10.012","volume":"18","author":"X Huang","year":"2014","unstructured":"Huang, X., et al.: Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 18(2), 253\u2013271 (2014)","journal-title":"Med. Image Anal."},{"key":"7_CR8","doi-asserted-by":"publisher","unstructured":"Lang, R.M., et al.: Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. Eur. Heart J. Cardiovascular Imag. 16(3), 233\u2013271 (2015). https:\/\/doi.org\/10.1093\/ehjci\/jev014","DOI":"10.1093\/ehjci\/jev014"},{"key":"7_CR9","doi-asserted-by":"publisher","unstructured":"Leclerc, S., Grenier, T., Espinosa, F., Bernard, O.: A fully automatic and multi-structural segmentation of the left ventricle and the myocardium on highly heterogeneous 2D echocardiographic data. In: 2017 IEEE International Ultrasonics Symposium (IUS), pp. 1\u20134 (2017). https:\/\/doi.org\/10.1109\/ULTSYM.2017.8092797","DOI":"10.1109\/ULTSYM.2017.8092797"},{"key":"7_CR10","doi-asserted-by":"publisher","unstructured":"Leclerc, S., et al.: Deep learning applied to multi-structure segmentation in 2D echocardiography: a preliminary investigation of the required database size. In: 2018 IEEE International Ultrasonics Symposium (IUS), pp. 1\u20134 (2018). https:\/\/doi.org\/10.1109\/ULTSYM.2018.8580136","DOI":"10.1109\/ULTSYM.2018.8580136"},{"issue":"9","key":"7_CR11","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imag. 38(9), 2198\u20132210 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2900516","journal-title":"IEEE Trans. Med. Imag."},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"106049","DOI":"10.1016\/j.asoc.2019.106049","volume":"88","author":"M Li","year":"2020","unstructured":"Li, M., et al.: Unified model for interpreting multi-view echocardiographic sequences without temporal information. Appl. Soft Comput. 88, 106049 (2020)","journal-title":"Appl. Soft Comput."},{"key":"7_CR13","unstructured":"Mehta, S., Ghazvininejad, M., Iyer, S., Zettlemoyer, L., Hajishirzi, H.: Delight: Deep and light-weight transformer, August 2020"},{"key":"7_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-030-58598-3_12","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Meng","year":"2020","unstructured":"Meng, Y., et al.: Regression of instance boundary by aggregated CNN and GCN. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12353, pp. 190\u2013207. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58598-3_12"},{"key":"7_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1007\/978-3-030-59719-1_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Meng","year":"2020","unstructured":"Meng, Y., et al.: CNN-GCN aggregation enabled boundary regression for biomedical image segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 352\u2013362. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_35"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2018","unstructured":"Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imag. 37(2), 384\u2013395 (2018). https:\/\/doi.org\/10.1109\/TMI.2017.2743464","journal-title":"IEEE Trans. Med. Imag."},{"key":"7_CR17","doi-asserted-by":"publisher","unstructured":"Ouyang, D., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252\u2013256 (2020). https:\/\/doi.org\/10.1038\/s41586-020-2145-8","DOI":"10.1038\/s41586-020-2145-8"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Reid, M., Marrese-Taylor, E., Matsuo, Y.: Subformer: Exploring weight sharing for parameter efficiency in generative transformers (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.344"},{"key":"7_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"},{"key":"7_CR20","unstructured":"Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., Beyer, L.: How to train your vit? data, augmentation, and regularization in vision transformers (2021)"},{"key":"7_CR21","doi-asserted-by":"publisher","unstructured":"Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted Res-UNet for high-quality retina vessel segmentation (2018). https:\/\/doi.org\/10.1109\/ITME.2018.00080","DOI":"10.1109\/ITME.2018.00080"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Xie, Y., Zhang, J., Shen, C., Xia, Y.: CoTr: Efficiently bridging CNN and transformer for 3D medical image segmentation, March 2021","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"7_CR23","unstructured":"Yang, Q.L.Z.Y.B.: SA-Net: Shuffle attention for deep convolutional neural networks, January 2021"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6881\u20136890, June 2021","DOI":"10.1109\/CVPR46437.2021.00681"}],"container-title":["Lecture Notes in Computer Science","Simplifying Medical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87583-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T22:45:28Z","timestamp":1673304328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87583-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875824","9783030875831"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87583-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASMUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advances in Simplifying Medical Ultrasound","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asmus2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-ultrasound.github.io\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","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":"22","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":"73% - 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":"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)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}