{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T07:37:51Z","timestamp":1776584271122,"version":"3.51.2"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["41674116"],"award-info":[{"award-number":["41674116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974126"],"award-info":[{"award-number":["41974126"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004147","name":"Tsinghua University","doi-asserted-by":"publisher","award":["2021Z99CFZ009"],"award-info":[{"award-number":["2021Z99CFZ009"]}],"id":[{"id":"10.13039\/501100004147","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["M21019"],"award-info":[{"award-number":["M21019"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFA0702501"],"award-info":[{"award-number":["2018YFA0702501"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers in Biology and Medicine"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1016\/j.compbiomed.2022.106090","type":"journal-article","created":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T22:25:52Z","timestamp":1662416752000},"page":"106090","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["An efficient annotated data generation method for echocardiographic image segmentation"],"prefix":"10.1016","volume":"149","author":[{"given":"Patrice","family":"Monkam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songbai","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenkai","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"10","key":"10.1016\/j.compbiomed.2022.106090_b1","doi-asserted-by":"crossref","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart disease and stroke statistics-2019 update: A report from the american heart association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"issue":"8","key":"10.1016\/j.compbiomed.2022.106090_b2","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1111\/echo.12832","article-title":"Quality control and reproducibility in M-mode, two-dimensional, and speckle tracking echocardiography acquisition and analysis: The CARDIA study, year 25 examination experience","volume":"32","author":"Armstrong","year":"2015","journal-title":"Echocardiography"},{"issue":"7","key":"10.1016\/j.compbiomed.2022.106090_b3","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1109\/TII.2018.2822680","article-title":"Detection of malicious code variants based on deep learning","volume":"4","author":"Cui","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.compbiomed.2022.106090_b4","article-title":"Architecture evolution of convolutional neural network using monarch butterfly optimization","author":"Wang","year":"2022","journal-title":"J. Ambient Intell. Human Comput."},{"key":"10.1016\/j.compbiomed.2022.106090_b5","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s00521-015-1874-3","article-title":"Self-adaptive extreme learning machine","volume":"27","author":"Wang","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.compbiomed.2022.106090_b6","doi-asserted-by":"crossref","unstructured":"Y. Hu, et al., Fully Automatic Pediatric Echocardiography Segmentation Using Deep Convolutional Networks Based on BiSeNet, in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, 2019, pp. 6561\u20136564.","DOI":"10.1109\/EMBC.2019.8856457"},{"issue":"9","key":"10.1016\/j.compbiomed.2022.106090_b7","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.compbiomed.2022.106090_b8","doi-asserted-by":"crossref","unstructured":"S. Leclerc, et al., RU-Net: A refining segmentation network for 2D echocardiography, in: 2019 IEEE International Ultrasonics Symposium, IUS, 2019, pp. 1160\u20131163.","DOI":"10.1109\/ULTSYM.2019.8926158"},{"issue":"169","key":"10.1016\/j.compbiomed.2022.106090_b9","doi-asserted-by":"crossref","DOI":"10.1098\/rsif.2020.0267","article-title":"Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks","volume":"17","author":"Arafati","year":"2020","journal-title":"J. R. Soc. Interface"},{"key":"10.1016\/j.compbiomed.2022.106090_b10","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.compbiomed.2022.106090_b11","series-title":"10th Nordicbaltic Conference on Biomedical Imaging, Vol. 4, Supplement 1, Part 1","first-page":"351","article-title":"Field: A program for simulating ultrasound systems","author":"Jensen","year":"1996"},{"key":"10.1016\/j.compbiomed.2022.106090_b12","series-title":"Freehand Ultrasound Image Simulation with Spatially Conditioned Generative Adversarial Networks","first-page":"105","volume":"vol. 10555 LNCS","author":"Hu","year":"2017"},{"key":"10.1016\/j.compbiomed.2022.106090_b13","doi-asserted-by":"crossref","unstructured":"F. Tom, D. Sheet, Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning, in: IEEE 15th International Symposium on Biomedical Imaging, ISBI, 2018, pp. 1174\u20131177.","DOI":"10.1109\/ISBI.2018.8363780"},{"key":"10.1016\/j.compbiomed.2022.106090_b14","doi-asserted-by":"crossref","unstructured":"B. Peng, X. Huang, S. Wang, J. Jiang, A Real-Time Medical Ultrasound Simulator Based on a Generative Adversarial Network Model, in: 2019 IEEE International Conference on Image Processing, ICIP, 2019, pp. 4629\u20134633.","DOI":"10.1109\/ICIP.2019.8803570"},{"key":"10.1016\/j.compbiomed.2022.106090_b15","doi-asserted-by":"crossref","unstructured":"G. Pigeau, et al., Ultrasound image simulation with generative adversarial network, in: Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 2020, p. 1131508.","DOI":"10.1117\/12.2549592"},{"key":"10.1016\/j.compbiomed.2022.106090_b16","doi-asserted-by":"crossref","first-page":"34442","DOI":"10.1109\/ACCESS.2021.3059595","article-title":"Early detection of myocardial infarction in low-quality echocardiography","volume":"9","author":"Degerli","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.compbiomed.2022.106090_b17","doi-asserted-by":"crossref","unstructured":"S.S. Ahn, et al., Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography, in: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021, pp. 348\u2013357.","DOI":"10.1007\/978-3-030-87193-2_33"},{"key":"10.1016\/j.compbiomed.2022.106090_b18","article-title":"Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotemporal semantic calibration and fusion","volume":"78","author":"Huisi","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2022.106090_b19","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","article-title":"Not-so-supervised: a survey of semisupervised, multi-instance, and transfer learning in medical image analysis","volume":"54","author":"Cheplygina","year":"2019","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compbiomed.2022.106090_b20","series-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","first-page":"235","article-title":"Fine tuning U-Net for ultrasound image segmentation: Which layers","author":"Amiri","year":"2019"},{"key":"10.1016\/j.compbiomed.2022.106090_b21","doi-asserted-by":"crossref","unstructured":"M. Amiri, et al., Fine-Tuning U-Net for Ultrasound Image Segmentation: Different Layers, Different Outcomes, in: IEEE Trans. on Ultra. Ferro. and Fre. Con., Vol. 67, No. 12, 2020, pp. 2510\u20132518.","DOI":"10.1109\/TUFFC.2020.3015081"},{"key":"10.1016\/j.compbiomed.2022.106090_b22","doi-asserted-by":"crossref","unstructured":"B. Behboodi, et al., Breast Lesion Segmentation in Ultrasound Images with Limited Annotated Data, in: 2020 IEEE 17th International Symposium on Biomedical Imaging, ISBI, 2020, pp. 1834\u20131837.","DOI":"10.1109\/ISBI45749.2020.9098685"},{"key":"10.1016\/j.compbiomed.2022.106090_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ultras.2022.106706","article-title":"CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data","volume":"122","author":"Song","year":"2022","journal-title":"Ultrasonics"},{"key":"10.1016\/j.compbiomed.2022.106090_b24","unstructured":"R. McGough, FOCUS: fast object-oriented C++ ultrasound simulator, [Online]. Available: https:\/\/www.egr.msu.edu\/fultras-web\/."},{"issue":"2","key":"10.1016\/j.compbiomed.2022.106090_b25","doi-asserted-by":"crossref","DOI":"10.1117\/1.3360308","article-title":"k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields","volume":"15","author":"Treeby","year":"2010","journal-title":"J. Bio. Opt."},{"key":"10.1016\/j.compbiomed.2022.106090_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105583","article-title":"Using deep learning to generate synthetic b-mode musculoskeletal ultrasound images","volume":"196","author":"Cronin","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.compbiomed.2022.106090_b27","doi-asserted-by":"crossref","unstructured":"L. Zhang, et al., Deep image translation for enhancing simulated ultrasound images, in: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, 2020, pp. 85\u201394.","DOI":"10.1007\/978-3-030-60334-2_9"},{"key":"10.1016\/j.compbiomed.2022.106090_b28","doi-asserted-by":"crossref","unstructured":"D. Tomar, et al., Content-Preserving Unpaired Translation from Simulated to Realistic Ultrasound Images, in: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis, MICCAI 2021, in: LNCS 12908, 2021, pp. 659\u2013669.","DOI":"10.1007\/978-3-030-87237-3_63"},{"key":"10.1016\/j.compbiomed.2022.106090_b29","first-page":"1","article-title":"Improving realism in patientspecific abdominal ultrasound simulation using CycleGANs","volume":"15","author":"Vitale","year":"2019","journal-title":"Int. J. Comp. Asst. Radiol. Surg."},{"key":"10.1016\/j.compbiomed.2022.106090_b30","doi-asserted-by":"crossref","unstructured":"J. Liang, et al., Synthesis and Edition of Ultrasound Images via Sketch Guided Progressive Growing GANS, in: IEEE 17th International Symposium on Biomedical Imaging, ISBI, 2020, pp. 1793\u20131797.","DOI":"10.1109\/ISBI45749.2020.9098384"},{"key":"10.1016\/j.compbiomed.2022.106090_b31","doi-asserted-by":"crossref","unstructured":"M. Sharifzadeh, et al., An Ultra-Fast Method for Simulation of Realistic Ultrasound Images, in: IEEE International Ultrasonics Symposium, IUS, 2022, pp. 1\u20134.","DOI":"10.1109\/IUS52206.2021.9593470"},{"key":"10.1016\/j.compbiomed.2022.106090_b32","doi-asserted-by":"crossref","unstructured":"Y. Yang, S. Soatto, FDA: Fourier Domain Adaptation for Semantic Segmentation, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2020, pp. 4084\u20134094.","DOI":"10.1109\/CVPR42600.2020.00414"},{"key":"10.1016\/j.compbiomed.2022.106090_b33","doi-asserted-by":"crossref","unstructured":"Q. Xu, et al., A Fourier-based Framework for Domain Generalization, in: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp. 14378\u201314387.","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"10.1016\/j.compbiomed.2022.106090_b34","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, et al., U-Net: Convolutional networks for biomedical image segmentation, in: Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervention, 2015, pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.compbiomed.2022.106090_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2- net: going deeper with nested u-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.compbiomed.2022.106090_b36","doi-asserted-by":"crossref","unstructured":"M. Jaramillo, et al., Endoscopic ultrasound database of the pancreas, in: Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 2020, p. 115830G.","DOI":"10.1117\/12.2581321"},{"key":"10.1016\/j.compbiomed.2022.106090_b37","series-title":"Do wide and deep networks learn the same things? Uncovering how neural network representations vary with width and depth","author":"Nguyen","year":"2021"},{"key":"10.1016\/j.compbiomed.2022.106090_b38","article-title":"Image segmentation algorithm of lung cancer based on neural network model","author":"He","year":"2021","journal-title":"Expert Syst."},{"issue":"2","key":"10.1016\/j.compbiomed.2022.106090_b39","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1109\/JBHI.2020.2997760","article-title":"Multi-scale context-guided deep network for automated lesion segmentation with endoscopy images of gastrointestinal tract","volume":"25","author":"Wang","year":"2021","journal-title":"IEEE J. Biomed. Health Info."},{"key":"10.1016\/j.compbiomed.2022.106090_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105618","article-title":"Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization","volume":"146","author":"Su","year":"2022","journal-title":"Comput. Bio. Med."},{"key":"10.1016\/j.compbiomed.2022.106090_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105810","article-title":"Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation","volume":"148","author":"Qi","year":"2022","journal-title":"Comput. Bio. Med."},{"key":"10.1016\/j.compbiomed.2022.106090_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105726","article-title":"How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective","author":"Wu","year":"2022","journal-title":"Comput. Bio. Med."},{"key":"10.1016\/j.compbiomed.2022.106090_b43","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1007\/s00521-015-1923-y","article-title":"Monarch butterfly optimization","volume":"31","author":"Wang","year":"2019","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"10.1016\/j.compbiomed.2022.106090_b44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJBIC.2018.093328","article-title":"Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems","volume":"12","author":"Wang","year":"2018","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"10.1016\/j.compbiomed.2022.106090_b45","doi-asserted-by":"crossref","unstructured":"G.G. Wang, S. Deb, L.S. Coelho, Elephant Herding Optimization, in: 2015 3rd International Symposium on Computational and Business Intelligence, ISCBI, 2015, pp. 1\u20135.","DOI":"10.1109\/ISCBI.2015.8"},{"key":"10.1016\/j.compbiomed.2022.106090_b46","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s12293-016-0212-3","article-title":"Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems","volume":"10","author":"Wang","year":"2018","journal-title":"Memetic Comp."},{"key":"10.1016\/j.compbiomed.2022.106090_b47","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","article-title":"Slime mould algorithm: A new method for stochastic optimization","volume":"111","author":"Li","year":"2020","journal-title":"Future Gen. Com. Syst."},{"issue":"114864","key":"10.1016\/j.compbiomed.2022.106090_b48","article-title":"Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts","volume":"177","author":"Yang","year":"2021","journal-title":"Exp. Syst. Appl."},{"issue":"115079","key":"10.1016\/j.compbiomed.2022.106090_b49","article-title":"RUN beyond the metaphor: An efficient optimization algorithm based on runge kutta method","volume":"181","author":"Ahmadianfar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compbiomed.2022.106090_b50","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1007\/s42235-021-0050-y","article-title":"The colony predation algorithm","volume":"18","author":"Tu","year":"2021","journal-title":"J. Bionic Eng."},{"key":"10.1016\/j.compbiomed.2022.106090_b51","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Fut. Generat. Comput. Syst."}],"container-title":["Computers in Biology and Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482522007983?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0010482522007983?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T19:57:45Z","timestamp":1757879865000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0010482522007983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10]]},"references-count":51,"alternative-id":["S0010482522007983"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106090","relation":{},"ISSN":["0010-4825"],"issn-type":[{"value":"0010-4825","type":"print"}],"subject":[],"published":{"date-parts":[[2022,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An efficient annotated data generation method for echocardiographic image segmentation","name":"articletitle","label":"Article Title"},{"value":"Computers in Biology and Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106090","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106090"}}