{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:27:54Z","timestamp":1770820074857,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100031478","name":"Next Generation EU","doi-asserted-by":"crossref","award":["Health From Portugal"],"award-info":[{"award-number":["Health From Portugal"]}],"id":[{"id":"10.13039\/100031478","id-type":"DOI","asserted-by":"crossref"}]},{"name":"FCT","award":["UIDB\/05549:2Ai"],"award-info":[{"award-number":["UIDB\/05549:2Ai"]}]},{"name":"FCT","award":["UIDP\/05549:2Ai"],"award-info":[{"award-number":["UIDP\/05549:2Ai"]}]},{"name":"FCT","award":["CEECINST\/00039\/2021"],"award-info":[{"award-number":["CEECINST\/00039\/2021"]}]},{"name":"FCT","award":["LASI-LA\/P\/0104\/2020"],"award-info":[{"award-number":["LASI-LA\/P\/0104\/2020"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82200557"],"award-info":[{"award-number":["82200557"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010434","name":"'la Caixa' Foundation","doi-asserted-by":"publisher","award":["LCF\/PR\/CI25\/10210"],"award-info":[{"award-number":["LCF\/PR\/CI25\/10210"]}],"id":[{"id":"10.13039\/100010434","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cardiovasc Eng Tech"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s13239-025-00816-8","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:37:00Z","timestamp":1767638220000},"page":"85-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating the Impact of Annotation Expertise on AI-Based Ultrasound Segmentation: A Case Study on Left Atrial Appendage"],"prefix":"10.1007","volume":"17","author":[{"given":"Rafael","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o L.","family":"Vila\u00e7a","sequence":"additional","affiliation":[]},{"given":"Helena R.","family":"Torres","sequence":"additional","affiliation":[]},{"given":"Yiting","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Alex Pui-Wai","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1995-7879","authenticated-orcid":false,"given":"Pedro","family":"Morais","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"816_CR1","doi-asserted-by":"publisher","first-page":"54310","DOI":"10.1109\/ACCESS.2021.3071301","volume":"9","author":"Y Wang","year":"2021","unstructured":"Wang, Y., X. Ge, H. Ma, S. Qi, G. Zhang, and Y. Yao. Deep learning in medical ultrasound image analysis: a review. IEEE Access. 9:54310\u201354324, 2021.","journal-title":"IEEE Access"},{"key":"816_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106629","volume":"215","author":"HR Torres","year":"2022","unstructured":"Torres, H. R., et al. A review of image processing methods for fetal head and brain analysis in ultrasound images. Comput. Methods Programs Biomed.215:106629, 2022.","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"816_CR3","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s40477-020-00557-5","volume":"24","author":"AE Ilesanmi","year":"2021","unstructured":"Ilesanmi, A. E., U. Chaumrattanakul, and S. S. Makhanov. Methods for the segmentation and classification of breast ultrasound images: a review. J. Ultrasound. 24(4):367\u2013382, 2021.","journal-title":"J. Ultrasound"},{"key":"816_CR4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen, C., et al. Deep learning for cardiac image segmentation: a review. Front. Cardiovasc. Med. 7:25, 2020.","journal-title":"Front. Cardiovasc. Med."},{"issue":"12","key":"816_CR5","doi-asserted-by":"publisher","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","volume":"46","author":"R Azad","year":"2024","unstructured":"Azad, R., et al. Medical image segmentation review: the success of U-Net. IEEE Trans. Pattern Anal. Mach. Intell. 46(12):10076\u201310095, 2024.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"816_CR6","doi-asserted-by":"crossref","unstructured":"Alloghani, M., D. Al-Jumeily, J. Mustafina, A. Hussain, and A. J. Aljaaf. A systematic review on supervised and unsupervised machine learning algorithms for data science. In: Supervised and Unsupervised Learning for Data Science. Cham: Springer, 2020, pp. 3\u201321.","DOI":"10.1007\/978-3-030-22475-2_1"},{"issue":"8","key":"816_CR7","doi-asserted-by":"publisher","first-page":"9284","DOI":"10.1109\/TPAMI.2023.3246102","volume":"45","author":"W Shen","year":"2023","unstructured":"Shen, W., et al. A survey on label-efficient deep image segmentation: bridging the gap between weak supervision and dense prediction. IEEE Trans. Pattern Anal. Mach. Intell. 45(8):9284\u20139305, 2023.","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"816_CR8","doi-asserted-by":"publisher","first-page":"8934","DOI":"10.1109\/TKDE.2022.3220219","volume":"35","author":"X Yang","year":"2022","unstructured":"Yang, X., Z. Song, I. King, and Z. Xu. A survey on deep semi-supervised learning. IEEE Trans. Knowl. Data Eng. 35(9):8934\u20138954, 2022.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"816_CR9","doi-asserted-by":"crossref","unstructured":"Tirindelli, M., C. Eilers, W. Simson, M. Paschali, M. F. Azampour, and N. Navab. Rethinking ultrasound augmentation: a physics-inspired approach. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021. Springer, 2021, pp. 690\u2013700.","DOI":"10.1007\/978-3-030-87237-3_66"},{"issue":"1.8","key":"816_CR10","doi-asserted-by":"publisher","first-page":"81","DOI":"10.14419\/ijet.v7i1.8.9977","volume":"7","author":"Y Reddy","year":"2018","unstructured":"Reddy, Y., P. Viswanath, and B. E. Reddy. Semi-supervised learning: a brief review. Int. J. Eng. Technol. 7(1.8):81, 2018.","journal-title":"Int. J. Eng. Technol."},{"key":"816_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2025.105558","volume":"168","author":"F Gao","year":"2025","unstructured":"Gao, F., A. Jiang, J. Liu, and J. Wang. Dynamic pseudo-label learning for semi-supervised ultrasound cardiac segmentation. Digit. Signal Process.168:105558, 2025.","journal-title":"Digit. Signal Process."},{"issue":"5","key":"816_CR12","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1007\/s10278-024-01344-y","volume":"38","author":"B Malainho","year":"2024","unstructured":"Malainho, B., et al. Semi-supervised ensemble learning for automatic interpretation of lung ultrasound videos. J. Imaging Inform. Med. 38(5):2664\u20132676, 2024.","journal-title":"J. Imaging Inform. Med."},{"issue":"1","key":"816_CR13","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12880-022-00793-7","volume":"22","author":"HE Kim","year":"2022","unstructured":"Kim, H. E., A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt. Transfer learning for medical image classification: a literature review. BMC Med. Imaging. 22(1):69, 2022.","journal-title":"BMC Med. Imaging"},{"key":"816_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110030","volume":"190","author":"M Usama","year":"2025","unstructured":"Usama, M., E. Nyman, U. N\u00e4slund, and C. Gr\u00f6nlund. A domain adaptation model for carotid ultrasound: image harmonization, noise reduction, and impact on cardiovascular risk markers. Comput. Biol. Med.190:110030, 2025.","journal-title":"Comput. Biol. Med."},{"issue":"12","key":"816_CR15","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1016\/j.jcmg.2014.08.009","volume":"7","author":"R Beigel","year":"2014","unstructured":"Beigel, R., N. C. Wunderlich, S. Y. Ho, R. Arsanjani, and R. J. Siegel. The left atrial appendage: anatomy, function, and noninvasive evaluation. JACC Cardiovasc. Imaging. 7(12):1251\u20131265, 2014.","journal-title":"JACC Cardiovasc. Imaging"},{"issue":"2","key":"816_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1093\/europace\/euz258","volume":"22","author":"M Glikson","year":"2020","unstructured":"Glikson, M., et al. EHRA\/EAPCI expert consensus statement on catheter-based left atrial appendage occlusion\u2014an update. EP Europace. 22(2):184, 2020.","journal-title":"EP Europace"},{"issue":"4","key":"816_CR17","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.jcmg.2015.02.002","volume":"8","author":"NC Wunderlich","year":"2015","unstructured":"Wunderlich, N. C., R. Beigel, M. J. Swaans, S. Y. Ho, and R. J. Siegel. Percutaneous interventions for left atrial appendage exclusion: options, assessment, and imaging using 2D and 3D echocardiography. JACC Cardiovasc. Imaging. 8(4):472\u2013488, 2015.","journal-title":"JACC Cardiovasc. Imaging"},{"issue":"6","key":"816_CR18","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1109\/JBHI.2018.2794552","volume":"22","author":"C Jin","year":"2018","unstructured":"Jin, C., et al. Left atrial appendage segmentation using fully convolutional neural networks and modified three-dimensional conditional random fields. IEEE J. Biomed. Health Inform. 22(6):1906\u20131916, 2018.","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"816_CR19","doi-asserted-by":"crossref","unstructured":"Morais, P., et al. 3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion. In: Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 2022, vol. 12034. SPIE, 2022, pp. 152\u2013159.","DOI":"10.1117\/12.2610705"},{"key":"816_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124727","volume":"255","author":"M Wu","year":"2024","unstructured":"Wu, M., D. Zhang, Y. Hua, M. Si, P. Liu, and Q. Wang. TransFusion: Efficient Vision Transformer based on 3D transesophageal echocardiography images for the left atrial appendage segmentation. Expert Syst. Appl.255:124727, 2024.","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"816_CR21","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.1109\/TUFFC.2018.2872816","volume":"65","author":"P Morais","year":"2018","unstructured":"Morais, P., et al. Fast segmentation of the left atrial appendage in 3-D transesophageal echocardiographic images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 65(12):2332\u20132342, 2018.","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"816_CR22","doi-asserted-by":"crossref","unstructured":"Fernandes, R., et al. Deep learning networks in the segmentation of the left atrial appendage in 2D ultrasound: a comparative analysis. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2023. IEEE, 2023, pp. 1\u20134.","DOI":"10.1109\/EMBC40787.2023.10340937"},{"issue":"17","key":"816_CR23","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.3390\/math11173771","volume":"11","author":"G Athanasiou","year":"2023","unstructured":"Athanasiou, G., J. L. Arcos, and J. Cerquides. Enhancing medical image segmentation: ground truth optimization through evaluating uncertainty in expert annotations. Mathematics. 11(17):3771, 2023.","journal-title":"Mathematics"},{"key":"816_CR24","first-page":"15750","volume":"33","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al. Disentangling human error from ground truth in segmentation of medical images. Adv. Neural Inf. Process. Syst. 33:15750\u201315762, 2020.","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"816_CR25","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.media.2013.02.006","volume":"17","author":"MJ Cardoso","year":"2013","unstructured":"Cardoso, M. J., et al. STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation. Med. Image Anal. 17(6):671\u2013684, 2013.","journal-title":"Med. Image Anal."},{"key":"816_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.983859","volume":"9","author":"MJ Sharkey","year":"2022","unstructured":"Sharkey, M. J., et al. Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning. Front. Cardiovasc. Med.9:983859, 2022.","journal-title":"Front. Cardiovasc. Med."},{"key":"816_CR27","doi-asserted-by":"crossref","unstructured":"Wu, Y., E. Wang, and Z. Shao. Fast abdomen organ and tumor segmentation with nn-UNet. In: MICCAI Challenge on Fast and Low-Resource Semi-supervised Abdominal Organ Segmentation, 2023. Springer, 2023, pp. 1\u201314.","DOI":"10.1007\/978-3-031-58776-4_1"},{"issue":"2","key":"816_CR28","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 18(2):203\u2013211, 2021.","journal-title":"Nat. Methods"},{"issue":"1","key":"816_CR29","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.echo.2021.08.023","volume":"35","author":"P Morais","year":"2022","unstructured":"Morais, P., Y. Fan, S. Queir\u00f3s, J. D\u2019hooge, A.P.-W. Lee, and J. L. Vila\u00e7a. Feasibility and accuracy of automated three-dimensional echocardiographic analysis of left atrial appendage for transcatheter closure. J. Am. Soc. Echocardiogr. 35(1):124\u2013133, 2022.","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"816_CR30","doi-asserted-by":"publisher","first-page":"1467180","DOI":"10.3389\/fnetp.2024.1467180","volume":"4","author":"MT Lee","year":"2024","unstructured":"Lee, M. T., et al. On preserving anatomical detail in statistical shape analysis for clustering: focus on left atrial appendage morphology. Front. Netw. Physiol. 4:1467180, 2024.","journal-title":"Front. Netw. Physiol."},{"issue":"5","key":"816_CR31","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1109\/TUFFC.2019.2903886","volume":"66","author":"P Morais","year":"2019","unstructured":"Morais, P., et al. Semiautomatic estimation of device size for left atrial appendage occlusion in 3-D TEE images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 66(5):922\u2013929, 2019.","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"}],"container-title":["Cardiovascular Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13239-025-00816-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13239-025-00816-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13239-025-00816-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T13:55:33Z","timestamp":1770818133000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13239-025-00816-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["816"],"URL":"https:\/\/doi.org\/10.1007\/s13239-025-00816-8","relation":{},"ISSN":["1869-408X","1869-4098"],"issn-type":[{"value":"1869-408X","type":"print"},{"value":"1869-4098","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"assertion":[{"value":"14 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The study received the approval of the Ethics Committee of the Prince of Wales Hospital (Code 2020.139).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}]}}