{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:09:20Z","timestamp":1778252960691,"version":"3.51.4"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870980"],"award-info":[{"award-number":["31870980"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.displa.2026.103449","type":"journal-article","created":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T22:57:10Z","timestamp":1774220230000},"page":"103449","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Uncertainty-aware point interaction for echocardiographic segmentation refinement"],"prefix":"10.1016","volume":"93","author":[{"given":"Chengcong","family":"Lv","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lianhuan","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yineng","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aihua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3872-0866","authenticated-orcid":false,"given":"Xingming","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103449_b0005","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.displa.2026.103449_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108100","article-title":"Boundary attention with multi-task consistency constraints for semi-supervised 2D echocardiography segmentation","volume":"171","author":"Zhao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.displa.2026.103449_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106705","article-title":"EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography","volume":"156","author":"Li","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.displa.2026.103449_b0020","doi-asserted-by":"crossref","unstructured":"G. Chen, G. Li, \u201cSemantic-aware temporal channel-wise attention for cardiac function assessment,\u201d in Proc. IEEE 19th Int. Symp. Biomed. Imaging (ISBI), (2022), pp. 1\u20134, doi: 10.1109\/ISBI52829.2022.9761481.","DOI":"10.1109\/ISBI52829.2022.9761481"},{"key":"10.1016\/j.displa.2026.103449_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105124","article-title":"FFANet\u2014Full frequency attention net for automatic diastolic function assessment","volume":"86","author":"Qu","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.displa.2026.103449_b0030","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentationMed","author":"Ronneberger","year":"2015","journal-title":"Image Comput. Comput.-Assist. Intervent. (MICCAI)"},{"key":"10.1016\/j.displa.2026.103449_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103241","article-title":"I2U-net: A dual-path U-Net with rich information interaction for medical image segmentation","volume":"97","author":"Dai","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.displa.2026.103449_b0040","doi-asserted-by":"crossref","unstructured":"K. R. Singh, A. Sharma, G. K. Singh, MADRU-Net: Multiscale Attention-Based Cardiac MRI Segmentation Using Deep Residual U-Net, in IEEE Transactions on Instrumentation and Measurement, 73 (2024), pp. 1-13, Art no. 2502413, doi: 10.1109\/TIM.2023.3332340.","DOI":"10.1109\/TIM.2023.3332340"},{"key":"10.1016\/j.displa.2026.103449_b0045","first-page":"1","article-title":"Semantic flow for fast and accurate scene parsing","volume":"2020","author":"Li","year":"2020","journal-title":"Comput Vis. \u2013 ECCV"},{"key":"10.1016\/j.displa.2026.103449_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106629","article-title":"HCTNet: a hybrid CNN-transformer network for breast ultrasound image segmentation","volume":"155","author":"He","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"10.1016\/j.displa.2026.103449_b0055","doi-asserted-by":"crossref","first-page":"4072","DOI":"10.1109\/TCSVT.2024.3523316","article-title":"CNN-transformer rectified collaborative learning for medical image segmentation","volume":"35","author":"Wu","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.displa.2026.103449_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2025.110410","article-title":"A structure adaptivity variation-based segmentation model for image with retinex and noise","volume":"241","author":"Wang","year":"2026","journal-title":"Signal Process."},{"key":"10.1016\/j.displa.2026.103449_b0065","doi-asserted-by":"crossref","unstructured":"X. Ma,et al., DOCNet: Dual-Domain Optimized Class-Aware Network for Remote Sensing Image Segmentation, IEEE Geoscience and Remote Sensing Letters, 21 (2024), pp. 1-5, Art no. 2500905, doi: 10.1109\/LGRS.2024.3350211.","DOI":"10.1109\/LGRS.2024.3350211"},{"key":"10.1016\/j.displa.2026.103449_b0070","doi-asserted-by":"crossref","unstructured":"A. Kirillov, et al., PointRend: Image segmentation as rendering, Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), (2020), pp. 9796\u20139805, doi: 10.1109\/CVPR42600.2020.00982.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"10.1016\/j.displa.2026.103449_b0075","unstructured":"Yarin Gal, Zoubin Ghahramani, Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, ICML, (2016) pp. 1050-1059, doi\/10.5555\/3045390.3045502."},{"key":"10.1016\/j.displa.2026.103449_b0080","article-title":"Uncertainty-aware Cross-Entropy for Semantic Segmentation","volume":"129\u2013136","author":"Landgraf","year":"2024","journal-title":"ISPRS Ann Photogramm. Remote Sens. Spatial Inf. Sci."},{"issue":"6","key":"10.1016\/j.displa.2026.103449_b0085","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0304771","article-title":"UDBRNet: a novel uncertainty driven boundary refined network for organ at risk segmentation","volume":"19","author":"Hassan","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.displa.2026.103449_b0090","doi-asserted-by":"crossref","unstructured":"H. Yang, L. Shen, M. Zhang, Q. Wang, Uncertainty-guided lung nodule segmentation with feature-aware attention, Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Singapore, (2022), pp. 44\u201353, doi: 10.1007\/978-3-031-16443-9_5.","DOI":"10.1007\/978-3-031-16443-9_5"},{"issue":"9","key":"10.1016\/j.displa.2026.103449_b0095","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/JBHI.2020.2972694","article-title":"Coarse-to-fine adversarial networks and zone-based uncertainty analysis for NK\/T-cell lymphoma segmentation in CT\/PET images","volume":"24","author":"Hu","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.displa.2026.103449_b0100","doi-asserted-by":"crossref","unstructured":"Y. Liu, Y. Tian, Y. Chen, F. Liu, V. Belagiannis, G. Carneiro, Perturbed and strict mean teachers for semi-supervised semantic segmentation, in: Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition (CVPR), (2022), New Orleans, LA, USA, pp. 4248\u20134257, doi: 10.1109\/CVPR52688.2022.00422.","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"10.1016\/j.displa.2026.103449_b0105","article-title":"CW-BASS: Confidence-weighted boundary-aware learning for semi-supervised semantic segmentation,\u201c","author":"Tarubinga","year":"2025","journal-title":"arXiv Preprint arXiv:2502.15152"},{"key":"10.1016\/j.displa.2026.103449_b0110","series-title":"Computer Vision \u2013 ECCV 2024 Lecture Notes in Computer Science","article-title":"Weighting pseudo-labels via high-activation feature index similarity and object detection for semi-supervised segmentation","author":"Howlader","year":"2025"},{"key":"10.1016\/j.displa.2026.103449_b0115","unstructured":"L.-C. Chen, et al., \u201cRethinking atrous convolution for semantic image segmentation,\u201d arXiv, (2017), doi: 10.48550\/arXiv.1706.05587."},{"key":"10.1016\/j.displa.2026.103449_b0120","doi-asserted-by":"crossref","unstructured":"C. Yu, et al., Bisenet: Bilateral segmentation network for real-time semantic segmentation, Proc. Eur. Conf. Comput. Vis. (ECCV), (2018), pp. 325\u2013341, doi: 10.1007\/978-3-030-01261-8_20.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"10.1016\/j.displa.2026.103449_b0125","unstructured":"A. Vaswani, et al., Attention is all you need, arXiv preprint arXiv:1706.03762, 2017. [Online]. Available: https:\/\/doi.org\/10.48550\/arXiv.1706.03762."},{"key":"10.1016\/j.displa.2026.103449_b0130","doi-asserted-by":"crossref","unstructured":"J. Gan, G. Zhang, J. Zhang, Y. Xiong, Y. Gan, Multiscale Neighborhood Cluster Scene Flow Prior for LiDAR Point Clouds, in: IEEE Transactions on Geoscience and Remote Sensing, 63 (2025) pp. 1-13, Art no. 5700613, doi: 10.1109\/TGRS.2024.3520209.","DOI":"10.1109\/TGRS.2024.3520209"},{"key":"10.1016\/j.displa.2026.103449_b0135","doi-asserted-by":"crossref","unstructured":"Z. Chen,et al., PointDC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super-Voxel Clustering, in: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, (2023), pp. 14244-14253, doi: 10.1109\/ICCV51070.2023.01314.","DOI":"10.1109\/ICCV51070.2023.01314"},{"key":"10.1016\/j.displa.2026.103449_b0140","article-title":"Image as set of points","author":"Ma","year":"2023","journal-title":"Int. Conf. Learn. Represent. (ICLR)"},{"key":"10.1016\/j.displa.2026.103449_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2025.110358","article-title":"A novel multi-means joint learning framework based on fuzzy clustering and self-constrained spectral clustering for superpixel image segmentation","volume":"124","author":"Wu","year":"2025","journal-title":"Comput. Electr. Eng."},{"issue":"9","key":"10.1016\/j.displa.2026.103449_b0150","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.displa.2026.103449_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.108611","article-title":"PMFSNet: Polarized multi-scale feature self-attention network for lightweight medical image segmentation","volume":"261","author":"Zhong","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.displa.2026.103449_b0160","doi-asserted-by":"crossref","unstructured":"Chao, C. -Y. Kao, Y. Ruan, C. -H. Huang, Y. -L. Lin, HarDNet: A Low Memory Traffic Network, in: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), (2019), pp. 3551-3560, doi: 10.1109\/ICCV.2019.00365.","DOI":"10.1109\/ICCV.2019.00365"},{"key":"10.1016\/j.displa.2026.103449_b0165","unstructured":"A. Paszke, et al., \u201cENet: A deep neural network architecture for real-time semantic segmentation,\u201d arXiv, (2016)."},{"issue":"7","key":"10.1016\/j.displa.2026.103449_b0170","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TCSVT.2024.3509504","article-title":"DSNet: a novel way to use atrous convolutions in semantic segmentation","volume":"35","author":"Guo","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.displa.2026.103449_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107773","article-title":"LiteNeXt: a novel lightweight ConvMixer-based model with self-embedding representation parallel for medical image segmentation","volume":"107","author":"Tran","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"10.1016\/j.displa.2026.103449_b0180","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad9106","article-title":"DESENet: a bilateral network with detail-enhanced semantic encoder for real-time semantic segmentation","volume":"36","author":"Tang","year":"2025","journal-title":"Meas. Sci. Technol."},{"issue":"2","key":"10.1016\/j.displa.2026.103449_b0185","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.70187","article-title":"DSGNet: a lightweight network integrating depthwise separable and ghost convolutions for real-time surface defect segmentation","volume":"43","author":"Lu","year":"2026","journal-title":"Expert. Syst."}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001125?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001125?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T14:09:30Z","timestamp":1778249370000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226001125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":37,"alternative-id":["S0141938226001125"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103449","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Uncertainty-aware point interaction for echocardiographic segmentation refinement","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103449","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103449"}}