{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:38:12Z","timestamp":1767418692059,"version":"3.48.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049803"},{"type":"electronic","value":"9783032049810"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04981-0_19","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:10:12Z","timestamp":1758258612000},"page":"194-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DPGS-Net: Dual Prior-Guided Cross-Domain Adaptive Framework for\u00a0Ultrasound Image Segmentation"],"prefix":"10.1007","author":[{"given":"Weijie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingfeng","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaonan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongyi","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"19_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., et al.: Medical image segmentation review: the success of u-net. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3435571"},{"key":"19_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107614","volume":"238","author":"H Bi","year":"2023","unstructured":"Bi, H., et al.: Bpat-unet: boundary preserving assembled transformer unet for ultrasound thyroid nodule segmentation. Comput. Methods Programs Biomed. 238, 107614 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"19_CR3","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"19_CR4","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"19_CR6","unstructured":"Cheng, J., et al.: Sam-med2d (2023). https:\/\/arxiv.org\/abs\/2308.16184"},{"key":"19_CR7","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Feng, W., Ju, L., Wang, L., Song, K., Zhao, X., Ge, Z.: Unsupervised domain adaptation for medical image segmentation by selective entropy constraints and adaptive semantic alignment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 623\u2013631 (2023)","DOI":"10.1609\/aaai.v37i1.25138"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Gong, H., et al.: Multi-task learning for thyroid nodule segmentation with thyroid region prior. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 257\u2013261. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434087"},{"key":"19_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106389","volume":"155","author":"H Gong","year":"2023","unstructured":"Gong, H., Chen, J., Chen, G., Li, H., Li, G., Chen, F.: Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput. Biol. Med. 155, 106389 (2023)","journal-title":"Comput. Biol. Med."},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"19_CR12","first-page":"5149","volume":"44","author":"T Hospedales","year":"2021","unstructured":"Hospedales, T., Antoniou, A., Micaelli, P., Storkey, A.: Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5149\u20135169 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Li, G., Zhang, Y., Luo, Y.: Multi-task cascaded attention network for brain tumor segmentation and classification. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2340\u20132344. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10447772"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"19_CR16","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","volume":"41","author":"Z Ning","year":"2021","unstructured":"Ning, Z., Zhong, S., Feng, Q., Chen, W., Zhang, Y.: Smu-net: saliency-guided morphology-aware u-net for breast lesion segmentation in ultrasound image. IEEE Trans. Med. Imaging 41(2), 476\u2013490 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR17","unstructured":"Oktay, O., et\u00a0al.: Attention u-net: learning where to look for the pancreas. arxiv. arXiv preprint arXiv:1804.03999 10 (2018)"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Pedraza, L., Vargas, C., Narv\u00e1ez, F., Dur\u00e1n, O., Mu\u00f1oz, E., Romero, E.: An open access thyroid ultrasound image database. In: 10th International Symposium on Medical Information Processing and Analysis, vol.\u00a09287, pp. 188\u2013193. SPIE (2015)","DOI":"10.1117\/12.2073532"},{"key":"19_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, Y., Zhang, X., Cheng, F.: Multi-task based category assisted gastrointestinal tumor segmentation. In: 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 1092\u20131096. IEEE (2024)","DOI":"10.1109\/ICSP62122.2024.10743865"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"19_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108972","volume":"180","author":"Y Yang","year":"2024","unstructured":"Yang, Y., Huang, H., Shao, Y., Chen, B.: Dac-net: a light-weight u-shaped network based efficient convolution and attention for thyroid nodule segmentation. Comput. Biol. Med. 180, 108972 (2024)","journal-title":"Comput. Biol. Med."},{"key":"19_CR25","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, R., Lu, W., Guan, C., Gao, J., Wei, X., Li, X.: Shan: shape guided network for thyroid nodule ultrasound cross-domain segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 732\u2013741. Springer (2024)","DOI":"10.1007\/978-3-031-72083-3_68"},{"key":"19_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103275","volume":"97","author":"B Zheng","year":"2024","unstructured":"Zheng, B., et al.: Dual domain distribution disruption with semantics preservation: unsupervised domain adaptation for medical image segmentation. Med. Image Anal. 97, 103275 (2024)","journal-title":"Med. Image Anal."},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Zheng, S., et\u00a0al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"19_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04981-0_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T05:33:31Z","timestamp":1767418411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04981-0_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049803","9783032049810"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04981-0_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}