{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:56:03Z","timestamp":1777290963011,"version":"3.51.4"},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003009","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["PKJ2025-Y04"],"award-info":[{"award-number":["PKJ2025-Y04"]}],"id":[{"id":"10.13039\/501100003009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003395","name":"Shanghai Municipal Education Commission","doi-asserted-by":"publisher","award":["JWAIYB-7"],"award-info":[{"award-number":["JWAIYB-7"]}],"id":[{"id":"10.13039\/501100003395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YG2025QNA07"],"award-info":[{"award-number":["YG2025QNA07"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Image and Vision Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.imavis.2026.105981","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T06:12:46Z","timestamp":1775455966000},"page":"105981","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Trustworthy cross-center semi-supervised echocardiographic segmentation via foundation models and uncertainty estimation"],"prefix":"10.1016","volume":"170","author":[{"given":"Bingqiang","family":"Han","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4115-8440","authenticated-orcid":false,"given":"Yiman","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Tongtong","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Lixin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yuqi","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunlan","family":"Xu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.imavis.2026.105981_b1","doi-asserted-by":"crossref","first-page":"e347","DOI":"10.1161\/CIR.0000000000001209","article-title":"2024 Heart disease and stroke statistics: a report of US and global data from the American Heart Association","volume":"149","author":"Martin","year":"2024","journal-title":"Circulation"},{"issue":"4","key":"10.1016\/j.imavis.2026.105981_b2","article-title":"Echocardiography: past, present, and future","volume":"17","author":"Gillam","year":"2024","journal-title":"Circ.: Cardiovasc. Imaging"},{"issue":"3","key":"10.1016\/j.imavis.2026.105981_b3","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s10278-024-00987-1","article-title":"Atrial septal defect detection in children based on ultrasound video using multiple instances learning","volume":"37","author":"Liu","year":"2024","journal-title":"J. Imaging Informatics Med."},{"issue":"10","key":"10.1016\/j.imavis.2026.105981_b4","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1109\/TMI.2022.3173669","article-title":"Echocardiography segmentation with enforced temporal consistency","volume":"41","author":"Painchaud","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.imavis.2026.105981_b5","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.imavis.2026.105981_b6","series-title":"MambaEviScrib: Mamba and evidence-guided consistency enhance CNN robustness for scribble-based weakly supervised ultrasound image segmentation","author":"Han","year":"2024"},{"key":"10.1016\/j.imavis.2026.105981_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105280","article-title":"Edmae: An efficient decoupled masked autoencoder for standard view identification in pediatric echocardiography","volume":"86","author":"Liu","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"issue":"3","key":"10.1016\/j.imavis.2026.105981_b8","doi-asserted-by":"crossref","DOI":"10.1002\/ima.23086","article-title":"Intelligent detection of left ventricular hypertrophy from pediatric echocardiography videos","volume":"34","author":"Liu","year":"2024","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"10.1016\/j.imavis.2026.105981_b9","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.imavis.2026.105981_b10","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine","first-page":"2004","article-title":"A semi-supervised approach with error reflection for echocardiography segmentation","author":"Han","year":"2024"},{"key":"10.1016\/j.imavis.2026.105981_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102517","article-title":"Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency","volume":"80","author":"Luo","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105981_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.123052","article-title":"Deep semi-supervised learning for medical image segmentation: A review","volume":"245","author":"Han","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105981_b13","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"297","article-title":"Semi-supervised left atrium segmentation with mutual consistency training","author":"Wu","year":"2021"},{"key":"10.1016\/j.imavis.2026.105981_b14","first-page":"8801","article-title":"Semi-supervised medical image segmentation through dual-task consistency","volume":"vol. 35","author":"Luo","year":"2021"},{"key":"10.1016\/j.imavis.2026.105981_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103111","article-title":"Mutual learning with reliable pseudo label for semi-supervised medical image segmentation","volume":"94","author":"Su","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105981_b16","article-title":"Pseudo labeling methods for semi-supervised semantic segmentation: A review and future perspectives","author":"Ran","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.imavis.2026.105981_b17","doi-asserted-by":"crossref","unstructured":"Q. Zeng, Y. Xie, Z. Lu, Y. Xia, Pefat: Boosting semi-supervised medical image classification via pseudo-loss estimation and feature adversarial training, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15671\u201315680.","DOI":"10.1109\/CVPR52729.2023.01504"},{"key":"10.1016\/j.imavis.2026.105981_b18","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"148","article-title":"Semi-supervised segmentation of liver using adversarial learning with deep atlas prior","author":"Zheng","year":"2019"},{"key":"10.1016\/j.imavis.2026.105981_b19","doi-asserted-by":"crossref","unstructured":"N. Souly, C. Spampinato, M. Shah, Semi supervised semantic segmentation using generative adversarial network, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 5688\u20135696.","DOI":"10.1109\/ICCV.2017.606"},{"key":"10.1016\/j.imavis.2026.105981_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.displa.2025.103152","article-title":"Semi-supervised echocardiography segmentation via cross-center invariant prior","author":"Xu","year":"2025","journal-title":"Displays"},{"key":"10.1016\/j.imavis.2026.105981_b21","doi-asserted-by":"crossref","unstructured":"A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A.C. Berg, W.-Y. Lo, et al., Segment anything, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 4015\u20134026.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"10.1016\/j.imavis.2026.105981_b22","series-title":"International Conference on Machine Learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.imavis.2026.105981_b23","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"368","article-title":"Sam-u: Multi-box prompts triggered uncertainty estimation for reliable sam in medical image","author":"Deng","year":"2023"},{"key":"10.1016\/j.imavis.2026.105981_b24","series-title":"2013 International Conference on Advanced Computer Science Applications and Technologies","first-page":"327","article-title":"Echocardiography image segmentation: A survey","author":"Mazaheri","year":"2013"},{"key":"10.1016\/j.imavis.2026.105981_b25","first-page":"1132","article-title":"Segmentation of the left heart ventricle in ultrasound images using a region based snake","volume":"vol. 8669","author":"Landgren","year":"2013"},{"issue":"21","key":"10.1016\/j.imavis.2026.105981_b26","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"issue":"9","key":"10.1016\/j.imavis.2026.105981_b27","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1111\/echo.14086","article-title":"Automation, machine learning, and artificial intelligence in echocardiography: a brave new world","volume":"35","author":"Gandhi","year":"2018","journal-title":"Echocardiography"},{"key":"10.1016\/j.imavis.2026.105981_b28","doi-asserted-by":"crossref","unstructured":"J. Domingos, R. Stebbing, J. Noble, Endocardial segmentation using structured random forests in 3D echocardiography, in: Proc. MICCAI Challenge Echocardiogr. Three-Dimensional Ultrasound Segmentation, CETUS, 2014, pp. 33\u201340.","DOI":"10.54294\/tw8mro"},{"key":"10.1016\/j.imavis.2026.105981_b29","series-title":"2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology","first-page":"110","article-title":"Comparison of unet architectures for segmentation of the left ventricle endocardial border on two-dimensional ultrasound images","author":"Zyuzin","year":"2019"},{"issue":"9","key":"10.1016\/j.imavis.2026.105981_b30","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","article-title":"Deep learning for segmentation using an open large-scale dataset in 2D echocardiography","volume":"38","author":"Leclerc","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.imavis.2026.105981_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102248","article-title":"Echocardiographic image segmentation using deep Res-U network","volume":"64","author":"Ali","year":"2021","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.imavis.2026.105981_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2022.106855","article-title":"MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography","volume":"127","author":"Zeng","year":"2023","journal-title":"Ultrasonics"},{"key":"10.1016\/j.imavis.2026.105981_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124727","article-title":"TransFusion: Efficient vision transformer based on 3D transesophageal echocardiography images for the left atrial appendage segmentation","volume":"255","author":"Wu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.imavis.2026.105981_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105407","article-title":"Cross-set data augmentation for semi-supervised medical image segmentation","volume":"154","author":"Wu","year":"2025","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105981_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2025.105640","article-title":"UniFormer: Consistency regularization-based semi-supervised semantic segmentation via differential dual-branch strongly augmented perturbations","author":"Qi","year":"2025","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105981_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105196","article-title":"Semi-supervised medical image segmentation via cross teaching between MobileNet and MobileViT","volume":"150","author":"Yang","year":"2024","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.imavis.2026.105981_b37","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ins.2018.12.057","article-title":"Disentangled variational auto-encoder for semi-supervised learning","volume":"482","author":"Li","year":"2019","journal-title":"Inform. Sci."},{"key":"10.1016\/j.imavis.2026.105981_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2021.103764","article-title":"Semi-supervised learning with GAN for automatic defect detection from images","volume":"128","author":"Zhang","year":"2021","journal-title":"Autom. Constr."},{"key":"10.1016\/j.imavis.2026.105981_b39","article-title":"Mutual consistency learning for semi-supervised medical image segmentation","volume":"81","author":"Wu","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.imavis.2026.105981_b40","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"450","article-title":"Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation","author":"Wang","year":"2021"},{"key":"10.1016\/j.imavis.2026.105981_b41","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.imavis.2026.105981_b42","article-title":"Segment together: A versatile paradigm for semi-supervised medical image segmentation","author":"Zeng","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.imavis.2026.105981_b43","first-page":"1","article-title":"Pick: Predict and mask for semi-supervised medical image segmentation","author":"Zeng","year":"2025","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.imavis.2026.105981_b44","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"226","article-title":"Exploring text-enhanced mixture-of-experts for semi-supervised medical image segmentation with composite data","author":"Zeng","year":"2025"},{"issue":"1","key":"10.1016\/j.imavis.2026.105981_b45","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/TMI.2024.3429340","article-title":"Consistency-guided differential decoding for enhancing semi-supervised medical image segmentation","volume":"44","author":"Zeng","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10.1016\/j.imavis.2026.105981_b46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3491209","article-title":"Trustworthy artificial intelligence: a review","volume":"55","author":"Kaur","year":"2022","journal-title":"ACM Comput. Surv."},{"issue":"4","key":"10.1016\/j.imavis.2026.105981_b47","first-page":"4396","article-title":"Domain generalization: A survey","volume":"45","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.imavis.2026.105981_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102830","article-title":"Trustworthy clinical AI solutions: A unified review of uncertainty quantification in deep learning models for medical image analysis","volume":"150","author":"Lambert","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"10.1016\/j.imavis.2026.105981_b49","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/TMI.2022.3224067","article-title":"Causality-inspired single-source domain generalization for medical image segmentation","volume":"42","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.imavis.2026.105981_b50","doi-asserted-by":"crossref","DOI":"10.1109\/JPROC.2024.3507831","article-title":"Domain generalization for medical image analysis: A review","author":"Yoon","year":"2024","journal-title":"Proc. IEEE"},{"issue":"3","key":"10.1016\/j.imavis.2026.105981_b51","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: a survey","volume":"69","author":"Guan","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.imavis.2026.105981_b52","article-title":"A simple baseline for bayesian uncertainty in deep learning","volume":"32","author":"Maddox","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.imavis.2026.105981_b53","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1038\/s41746-022-00709-3","article-title":"Improving the repeatability of deep learning models with Monte Carlo dropout","volume":"5","author":"Lemay","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.imavis.2026.105981_b54","article-title":"Evidential deep learning to quantify classification uncertainty","volume":"31","author":"Sensoy","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.imavis.2026.105981_b55","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"605","article-title":"Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation","author":"Yu","year":"2019"},{"key":"10.1016\/j.imavis.2026.105981_b56","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"318","article-title":"Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency","author":"Luo","year":"2021"},{"key":"10.1016\/j.imavis.2026.105981_b57","series-title":"European Conference on Computer Vision","first-page":"424","article-title":"When cnn meet with vit: Towards semi-supervised learning for multi-class medical image semantic segmentation","author":"Wang","year":"2022"},{"key":"10.1016\/j.imavis.2026.105981_b58","doi-asserted-by":"crossref","unstructured":"Y. Bai, D. Chen, Q. Li, W. Shen, Y. Wang, Bidirectional copy-paste for semi-supervised medical image segmentation, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 11514\u201311524.","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"10.1016\/j.imavis.2026.105981_b59","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"567","article-title":"Sdcl: Students discrepancy-informed correction learning for semi-supervised medical image segmentation","author":"Song","year":"2024"},{"key":"10.1016\/j.imavis.2026.105981_b60","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107415","article-title":"CCA: Contrastive cluster assignment for supervised and semi-supervised medical image segmentation","volume":"188","author":"Zhu","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.imavis.2026.105981_b61","article-title":"Unimatch v2: Pushing the limit of semi-supervised semantic segmentation","author":"Yang","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626000880?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626000880?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:22:04Z","timestamp":1777288924000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0262885626000880"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":61,"alternative-id":["S0262885626000880"],"URL":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105981","relation":{},"ISSN":["0262-8856"],"issn-type":[{"value":"0262-8856","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Trustworthy cross-center semi-supervised echocardiographic segmentation via foundation models and uncertainty estimation","name":"articletitle","label":"Article Title"},{"value":"Image and Vision Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.imavis.2026.105981","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"105981"}}