{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:13:06Z","timestamp":1790399586874,"version":"4.1.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723773","type":"print"},{"value":"9783031723780","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72378-0_42","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T07:02:53Z","timestamp":1727852573000},"page":"448-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Intrapartum Ultrasound Image Segmentation of\u00a0Pubic Symphysis and\u00a0Fetal Head Using Dual Student-Teacher Framework with\u00a0CNN-ViT Collaborative Learning"],"prefix":"10.1007","author":[{"given":"Jianmei","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jieyun","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor M.","family":"Campello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"issue":"14","key":"42_CR1","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1080\/14767058.2020.1799974","volume":"35","author":"L Angeli","year":"2022","unstructured":"Angeli, L., et al.: Automatic measurement of head-perineum distance during intrapartum ultrasound: description of the technique and preliminary results. J. Mater.-Fetal Neonatal Med. 35(14), 2759\u20132764 (2022)","journal-title":"J. Mater.-Fetal Neonatal Med."},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Bai, J., Lu, Y., Liu, H., He, F., Guo, X.: New technologies improve maternal and newborn safety (2024)","DOI":"10.3389\/fmedt.2024.1372358"},{"key":"42_CR3","doi-asserted-by":"publisher","first-page":"2565","DOI":"10.3389\/fphys.2022.940150","volume":"13","author":"J Bai","year":"2022","unstructured":"Bai, J., et al.: A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network. Front. Physiol. 13, 2565 (2022)","journal-title":"Front. Physiol."},{"issue":"1","key":"42_CR4","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/s41597-024-03266-4","volume":"11","author":"G Chen","year":"2024","unstructured":"Chen, G., Bai, J., Ou, Z., Lu, Y., Wang, H.: PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head. Sci. Data 11(1), 436 (2024)","journal-title":"Sci. Data"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"42_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Lu, Y., Long, S., Campello, V.M., Bai, J., Lekadir, K.: Fetal head and pubic symphysis segmentation in intrapartum ultrasound image using a dual-path boundary-guided residual network. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3399762"},{"key":"42_CR7","doi-asserted-by":"publisher","first-page":"123096","DOI":"10.1016\/j.eswa.2023.123096","volume":"245","author":"Z Chen","year":"2024","unstructured":"Chen, Z., Ou, Z., Lu, Y., Bai, J.: Direction-guided and multi-scale feature screening for fetal head-pubic symphysis segmentation and angle of progression calculation. Expert Syst. Appl. 245, 123096 (2024)","journal-title":"Expert Syst. Appl."},{"key":"42_CR8","doi-asserted-by":"crossref","unstructured":"Li, S., Lv, F., Xie, B., Liu, C.H., Liang, J., Qin, C.: Bi-classifier determinacy maximization for unsupervised domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 8455\u20138464 (2021)","DOI":"10.1609\/aaai.v35i10.17027"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Li, W., Yang, H.: Collaborative transformer-CNN learning for semi-supervised medical image segmentation. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1058\u20131065 (2022)","DOI":"10.1109\/BIBM55620.2022.9995501"},{"key":"42_CR10","doi-asserted-by":"publisher","first-page":"107904","DOI":"10.1016\/j.dib.2022.107904","volume":"41","author":"Y Lu","year":"2022","unstructured":"Lu, Y., et al.: The JNU-IFM dataset for segmenting pubic symphysis-fetal head. Data Brief 41, 107904 (2022)","journal-title":"Data Brief"},{"key":"42_CR11","unstructured":"Luo, X., Hu, M., Song, T., Wang, G., Zhang, S.: Semi-supervised medical image segmentation via cross teaching between CNN and transformer. In: International Conference on Medical Imaging with Deep Learning, pp. 820\u2013833 (2022)"},{"key":"42_CR12","doi-asserted-by":"publisher","first-page":"108501","DOI":"10.1016\/j.compbiomed.2024.108501","volume":"175","author":"Z Ou","year":"2024","unstructured":"Ou, Z., Bai, J., Chen, Z., Lu, Y., Wang, H., Long, S., Chen, G.: RTSeg-net: a lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images. Comput. Biol. Med. 175, 108501 (2024)","journal-title":"Comput. Biol. Med."},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12674\u201312684 (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"42_CR14","doi-asserted-by":"crossref","unstructured":"Qiao, S., Shen, W., Zhang, Z., Wang, B., Yuille, A.: Deep co-training for semi-supervised image recognition. In: Proceedings of the European conference on computer vision (eccv), pp. 135\u2013152 (2018)","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"42_CR15","doi-asserted-by":"crossref","unstructured":"Qiu, R., Zhou, M., Bai, J., Lu, Y., Wang, H.: PSFHSP-Net: an efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images. Med. Biol. Eng. Comput. 1\u201312 (2024)","DOI":"10.1007\/s11517-024-03111-1"},{"key":"42_CR16","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"42_CR17","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90\u2013106 (2022)","journal-title":"Neural Netw."},{"key":"42_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, T., Zheng, J.Q., Huang, B.: When CNN meet with ViT: towards semi-supervised learning for multi-class medical image semantic segmentation. In: European Conference on Computer Vision, pp. 424\u2013441 (2022)","DOI":"10.1007\/978-3-031-25082-8_28"},{"key":"42_CR19","doi-asserted-by":"publisher","first-page":"102530","DOI":"10.1016\/j.media.2022.102530","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu, Y., et al.: Mutual consistency learning for semi-supervised medical image segmentation. Med. Image Anal. 81, 102530 (2022)","journal-title":"Med. Image Anal."},{"key":"42_CR20","first-page":"6256","volume":"33","author":"Q Xie","year":"2020","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. Adv. Neural. Inf. Process. Syst. 33, 6256\u20136268 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"42_CR21","doi-asserted-by":"crossref","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22, pp. 605\u2013613 (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"42_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, pp. 408\u2013416 (2017)","DOI":"10.1007\/978-3-319-66179-7_47"},{"issue":"5","key":"42_CR23","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s11517-022-02747-1","volume":"61","author":"M Zhou","year":"2023","unstructured":"Zhou, M., et al.: The segmentation effect of style transfer on fetal head ultrasound image: a study of multi-source data. Med. Biol. Eng. Comput. 61(5), 1017\u20131031 (2023)","journal-title":"Med. Biol. Eng. Comput."},{"key":"42_CR24","doi-asserted-by":"crossref","unstructured":"Zhou, M., Yuan, C., Chen, Z., Wang, C., Lu, Y.: Automatic angle of progress measurement of intrapartum transperineal ultrasound image with deep learning. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VI 23, pp. 406\u2013414 (2020)","DOI":"10.1007\/978-3-030-59725-2_39"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72378-0_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T07:30:33Z","timestamp":1771054233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72378-0_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723773","9783031723780"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72378-0_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}