{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:21:39Z","timestamp":1766578899249,"version":"3.44.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0706003"],"award-info":[{"award-number":["2022YFF0706003"]}]},{"name":"Tianjin Science and Technology Project","award":["20YDTPJC01110"],"award-info":[{"award-number":["20YDTPJC01110"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s44443-025-00206-z","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T15:27:50Z","timestamp":1755617270000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GRU2-Net: Global response double U-shaped network for lesion segmentation in ultrasound images"],"prefix":"10.1007","volume":"37","author":[{"given":"Xiaokai","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuewen","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinying","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"206_CR1","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani W et al (2020) Dataset of breast ultrasound images. Data Brief 28:104863","journal-title":"Data Brief"},{"issue":"12","key":"206_CR2","first-page":"234","volume":"3","author":"K Bhargavi","year":"2014","unstructured":"Bhargavi K, Jyothi S (2014) A survey on threshold based segmentation technique in image processing. Int J Innov Res Dev 3(12):234\u2013239","journal-title":"Int J Innov Res Dev"},{"key":"206_CR3","doi-asserted-by":"crossref","unstructured":"Cao H et al (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision. pp 205\u2013218","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"206_CR4","doi-asserted-by":"crossref","unstructured":"Carion N et al (2020) End-to-end object detection with transformers. In: European conference on computer vision. pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"issue":"5","key":"206_CR5","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TMI.2022.3226268","volume":"42","author":"G Chen","year":"2022","unstructured":"Chen G et al (2022) AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images. IEEE Trans Med Imaging 42(5):1289\u20131300","journal-title":"IEEE Trans Med Imaging"},{"key":"206_CR6","doi-asserted-by":"publisher","first-page":"123265","DOI":"10.1016\/j.eswa.2024.123265","volume":"246","author":"G Chen","year":"2024","unstructured":"Chen G et al (2024) ESKNet: an enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation. Expert Syst Appl 246:123265","journal-title":"Expert Syst Appl"},{"key":"206_CR7","unstructured":"Chen J et al (2021) Transunet: Transformers make strong encoders for medical image segmentation.\u00a0arXiv preprint arXiv:2102.04306"},{"key":"206_CR8","doi-asserted-by":"crossref","unstructured":"Chowdary GJ, Yoagarajah P (2023) EU-Net: Enhanced U-shaped network for breast mass segmentation. IEEE Journal of Biomedical and Health Informatics","DOI":"10.36227\/techrxiv.20036630"},{"issue":"7","key":"206_CR9","first-page":"86","volume":"2","author":"P Dhankhar","year":"2013","unstructured":"Dhankhar P, Sahu N (2013) A review and research of edge detection techniques for image segmentation. Int J Comput Sci Mob Comput 2(7):86\u201392","journal-title":"Int J Comput Sci Mob Comput"},{"key":"206_CR10","unstructured":"Dosovitskiy A (2020) An image is worth 16x16 words: Transformers for image recognition at scale.\"arXiv preprint arXiv:2010.11929"},{"key":"206_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106629","volume":"155","author":"Q He","year":"2023","unstructured":"He Q, Yang Q, Xie M (2023) HCTNet: a hybrid CNN-transformer network for breast ultrasound image segmentation. Comput Biol Med 155:106629","journal-title":"Comput Biol Med"},{"issue":"11","key":"206_CR12","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.3390\/rs16111907","volume":"16","author":"Kai Hu","year":"2024","unstructured":"Hu Kai et al (2024) An interpolation and prediction algorithm for XCO2 based on multi-source time series data. Remote Sens 16(11):1907","journal-title":"Remote Sens"},{"key":"206_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128015","author":"K Hu","year":"2024","unstructured":"Hu K et al (2024) An overview: attention mechanisms in multi-agent reinforcement learning. Neurocomputing\u00a0598:128015","journal-title":"Neurocomputing"},{"key":"206_CR14","doi-asserted-by":"crossref","unstructured":"Hu K et al (2025) Enhancing underwater video from consecutive frames while preserving temporal consistency. J Mar Sci Eng 13(1):127","DOI":"10.3390\/jmse13010127"},{"issue":"1","key":"206_CR15","volume":"2018","author":"Q Huang","year":"2018","unstructured":"Huang Q, Zhang F, Li X (2018) Machine learning in ultrasound computer-aided diagnostic systems: a survey. Biomed Res Int 2018(1):5137904","journal-title":"Biomed Res Int"},{"key":"206_CR16","doi-asserted-by":"crossref","unstructured":"Huang G et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"206_CR17","doi-asserted-by":"crossref","unstructured":"Huang H et al (2020) Unet 3+: A full-scale connected unet for medical image segmentation. In:\u00a0ICASSP 2020\u20132020 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp 1055\u20131059","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"206_CR18","doi-asserted-by":"crossref","unstructured":"Jha D et al (2019) Resunet++: an advanced architecture for medical image segmentation. In:\u00a02019 IEEE international symposium on multimedia (ISM). pp\u00a0225\u20132255","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"206_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3304662","author":"H Kuang","year":"2023","unstructured":"Kuang H et al (2023) Bea-net: body and edge aware network with multi-scale short-term concatenation for medical image segmentation. IEEE J Biomed Health Inform\u00a027(10):4828-4839","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"206_CR20","first-page":"1344","volume":"67","author":"H Lee","year":"2020","unstructured":"Lee H, Park J, Hwang JH (2020) Channel attention module with multiscale grid average pooling for breast cancer segmentation in an ultrasound image. IEEE Trans Ultrason Ferroelectr Freq Control 67(7):1344\u20131353","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"206_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107186","volume":"227","author":"X Lin","year":"2022","unstructured":"Lin X et al (2022) A super-resolution guided network for improving automated thyroid nodule segmentation. Comput Methods Programs Biomed 227:107186","journal-title":"Comput Methods Programs Biomed"},{"key":"206_CR22","doi-asserted-by":"crossref","unstructured":"Liu Z et al 2021 Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp\u00a010012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"8","key":"206_CR23","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1109\/TMI.2006.877092","volume":"25","author":"JA Noble","year":"2006","unstructured":"Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987\u20131010","journal-title":"IEEE Trans Med Imaging"},{"key":"206_CR24","doi-asserted-by":"crossref","unstructured":"Pedraza L et al (2015) An open access thyroid ultrasound image database.\u00a0In: 10th International symposium on medical information processing and analysis, vol 9287. pp 188\u2013193","DOI":"10.1117\/12.2073532"},{"key":"206_CR25","doi-asserted-by":"publisher","first-page":"102430","DOI":"10.1016\/j.inffus.2024.102430","volume":"109","author":"X Qu","year":"2024","unstructured":"Qu X et al (2024) EH-former: regional easy-hard-aware transformer for breast lesion segmentation in ultrasound images. Inf Fusion 109:102430","journal-title":"Inf Fusion"},{"issue":"1","key":"206_CR26","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/s10479-022-04755-8","volume":"328","author":"R Ranjbarzadeh","year":"2023","unstructured":"Ranjbarzadeh R et al (2023) MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in mammograms using a convolutional neural network. Ann Oper Res 328(1):1021\u20131042","journal-title":"Ann Oper Res"},{"key":"206_CR27","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In:\u00a0Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18. pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"206_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3421436","author":"A Roy","year":"2024","unstructured":"Roy A, Pramanik P, Sarkar R (2024) EU 2\u2013net: a parameter efficient ensemble model with attention-aided triple feature fusion for tumor segmentation in breast ultrasound images. IEEE Trans Instrum Meas\u00a073:1\u20137","journal-title":"IEEE Trans Instrum Meas"},{"key":"206_CR29","doi-asserted-by":"crossref","unstructured":"Shi D (2024) TransNeXt: robust foveal visual perception for vision transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 17773\u201317783","DOI":"10.1109\/CVPR52733.2024.01683"},{"key":"206_CR30","doi-asserted-by":"crossref","unstructured":"Strudel R et al (2021) Segmenter: Transformer for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 7262\u20137272","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"206_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105917","volume":"91","author":"S Sun","year":"2024","unstructured":"Sun S et al (2024) Crsanet: class representations self-attention network for the segmentation of thyroid nodules. Biomed Signal Process Control 91:105917","journal-title":"Biomed Signal Process Control"},{"key":"206_CR32","doi-asserted-by":"crossref","unstructured":"Tang F et al (2023) Cmu-net: a strong convmixer-based medical ultrasound image segmentation network. In: IEEE 20th international symposium on biomedical imaging (ISBI). pp 1\u20135","DOI":"10.1109\/ISBI53787.2023.10230609"},{"issue":"16","key":"206_CR33","doi-asserted-by":"publisher","first-page":"5984","DOI":"10.3390\/s22165984","volume":"22","author":"Z Tao","year":"2022","unstructured":"Tao Z et al (2022) Local and context-attention adaptive LCA-Net for thyroid nodule segmentation in ultrasound images. Sensors (Basel) 22(16):5984","journal-title":"Sensors (Basel)"},{"issue":"1","key":"206_CR34","first-page":"261","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani A et al (2017) Attention is all you need. Adv Neural Inf Process Syst 30(1):261\u2013272","journal-title":"Adv Neural Inf Process Syst"},{"key":"206_CR35","doi-asserted-by":"publisher","first-page":"124835","DOI":"10.1016\/j.eswa.2024.124835","volume":"256","author":"Yaqi Wang","year":"2024","unstructured":"Wang Yaqi et al (2024) Graph neural network enhanced dual-branch network for lesion segmentation in ultrasound images. Expert Syst Appl 256:124835","journal-title":"Expert Syst Appl"},{"key":"206_CR36","doi-asserted-by":"publisher","first-page":"123798","DOI":"10.1016\/j.eswa.2024.123798","volume":"249","author":"J Wang","year":"2024","unstructured":"Wang J et al (2024) MF-Net: multiple-feature extraction network for breast lesion segmentation in ultrasound images. Expert Syst Appl 249:123798","journal-title":"Expert Syst Appl"},{"key":"206_CR37","doi-asserted-by":"crossref","unstructured":"Wang Z et al (2022) Uformer: a general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 17683\u201317693","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"206_CR38","doi-asserted-by":"crossref","unstructured":"Wu H et al (2021) Cvt: Introducing convolutions to vision transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 22\u201331","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"206_CR39","doi-asserted-by":"publisher","first-page":"102370","DOI":"10.1016\/j.compmedimag.2024.102370","volume":"114","author":"M Xu","year":"2024","unstructured":"Xu M et al (2024) MEF-UNet: an end-to-end ultrasound image segmentation algorithm based on multi-scale feature extraction and fusion. Comput Medi Imaging Graph 114:102370","journal-title":"Comput Medi Imaging Graph"},{"issue":"39","key":"206_CR40","doi-asserted-by":"publisher","first-page":"28525","DOI":"10.1007\/s11042-020-09311-9","volume":"79","author":"H Yu","year":"2020","unstructured":"Yu H, He F, Pan Y (2020) A survey of level set method for image segmentation with intensity inhomogeneity. Multimedia Tools Appl 79(39):28525\u201328549","journal-title":"Multimedia Tools Appl"},{"key":"206_CR41","doi-asserted-by":"crossref","unstructured":"Yuan K et al (2021) Incorporating convolution designs into visual transformers. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 579\u2013588","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"206_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105427","volume":"87","author":"H Zhang","year":"2024","unstructured":"Zhang H et al (2024) Hau-net: hybrid CNN-transformer for breast ultrasound image segmentation. Biomed Signal Process Control 87:105427","journal-title":"Biomed Signal Process Control"},{"issue":"6","key":"206_CR43","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z et al (2019) Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Medi Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Medi Imaging"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00206-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00206-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00206-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T12:45:19Z","timestamp":1758113119000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00206-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":43,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["206"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00206-z","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"type":"print","value":"1319-1578"},{"type":"electronic","value":"2213-1248"}],"subject":[],"published":{"date-parts":[[2025,8,19]]},"assertion":[{"value":"22 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"186"}}