{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:23:20Z","timestamp":1769387000259,"version":"3.49.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049834","type":"print"},{"value":"9783032049841","type":"electronic"}],"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\/978-3-032-04984-1_47","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:00Z","timestamp":1758299100000},"page":"487-496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semi-supervised Multi-modal Medical Image Segmentation for\u00a0Complex Situations"],"prefix":"10.1007","author":[{"given":"Dongdong","family":"Meng","sequence":"first","affiliation":[]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Guoping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xueqing","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"102506","DOI":"10.1016\/j.media.2022.102506","volume":"80","author":"X Chen","year":"2022","unstructured":"Chen, X., Zhou, H.Y., Liu, F., Guo, J., Wang, L., Yu, Y.: Mass: modality-collaborative semi-supervised segmentation by exploiting cross-modal consistency from unpaired ct and mri images. Med. Image Anal. 80, 102506 (2022)","journal-title":"Med. Image Anal."},{"key":"47_CR2","doi-asserted-by":"publisher","unstructured":"Gao, S., Zhang, Z., Ma, J., Li, Z., Zhang, S.: Correlation-aware mutual learning for semi-supervised medical image segmentation. In: Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., Taylor, R. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 98\u2013108. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43907-0_10","DOI":"10.1007\/978-3-031-43907-0_10"},{"key":"47_CR3","doi-asserted-by":"publisher","first-page":"123052","DOI":"10.1016\/j.eswa.2023.123052","volume":"245","author":"K Han","year":"2024","unstructured":"Han, K., Sheng, V.S., Song, Y., Liu, Y., Qiu, C., Ma, S., Liu, Z.: Deep semi-supervised learning for medical image segmentation: a review. Expert Syst. Appl. 245, 123052 (2024)","journal-title":"Expert Syst. Appl."},{"key":"47_CR4","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00745","DOI":"10.1109\/CVPR.2018.00745"},{"key":"47_CR5","doi-asserted-by":"publisher","unstructured":"Li, D., Yang, B., Zhan, W., He, X.: Multi-category graph reasoning for multi-modal brain tumor segmentation. In: Linguraru, M.G., Dou, Q., Feragen, A., Giannarou, S., Glocker, B., Lekadir, K., Schnabel, J.A. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 445\u2013455. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_42","DOI":"10.1007\/978-3-031-72111-3_42"},{"key":"47_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2024.3493878","volume":"73","author":"Z Li","year":"2024","unstructured":"Li, Z., Huang, C., Xie, S.: Multimodality-assisted semi-supervised brain tumor segmentation in nondominant modality based on consistency learning. IEEE Trans. Instrum. Meas. 73, 1\u201311 (2024)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"47_CR7","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1148\/radiol.2019182012","volume":"291","author":"L Lin","year":"2019","unstructured":"Lin, L., et al.: Deep learning for automated contouring of primary tumor volumes by mri for nasopharyngeal carcinoma. Radiology 291(3), 677\u2013686 (2019)","journal-title":"Radiology"},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 8801\u20138809 (2021)","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"47_CR9","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. PMLR (2022)"},{"key":"47_CR10","doi-asserted-by":"publisher","first-page":"102517","DOI":"10.1016\/j.media.2022.102517","volume":"80","author":"X Luo","year":"2022","unstructured":"Luo, X., et al.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Med. Image Anal. 80, 102517 (2022)","journal-title":"Med. Image Anal."},{"issue":"8","key":"47_CR11","doi-asserted-by":"publisher","first-page":"3183","DOI":"10.1007\/s00371-023-02965-0","volume":"39","author":"D Meng","year":"2023","unstructured":"Meng, D., et al.: 3d reconstruction-oriented fully automatic multi-modal tumor segmentation by dual attention-guided vnet. Vis. Comput. 39(8), 3183\u20133196 (2023)","journal-title":"Vis. Comput."},{"issue":"10","key":"47_CR12","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: Weber: the multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"47_CR13","doi-asserted-by":"publisher","first-page":"9284","DOI":"10.1109\/TPAMI.2023.3246102","volume":"45","author":"W Shen","year":"2023","unstructured":"Shen, W., et al.: A survey on label-efficient deep image segmentation: bridging the gap between weak supervision and dense prediction. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 9284\u20139305 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"47_CR14","doi-asserted-by":"publisher","first-page":"103111","DOI":"10.1016\/j.media.2024.103111","volume":"94","author":"J Su","year":"2024","unstructured":"Su, J., Luo, Z., Lian, S., Lin, D., Li, S.: Mutual learning with reliable pseudo label for semi-supervised medical image segmentation. Med. Image Anal. 94, 103111 (2024)","journal-title":"Med. Image Anal."},{"key":"47_CR15","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems. vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"47_CR16","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":"47_CR17","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2512\u20132521 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"47_CR18","doi-asserted-by":"publisher","first-page":"102263","DOI":"10.1016\/j.inffus.2024.102263","volume":"106","author":"Y Weng","year":"2024","unstructured":"Weng, Y., Zhang, Y., Wang, W., Dening, T.: Semi-supervised information fusion for medical image analysis: recent progress and future perspectives. Inf. Fusion 106, 102263 (2024)","journal-title":"Inf. Fusion"},{"key":"47_CR19","doi-asserted-by":"publisher","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: Shen, D., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"47_CR20","doi-asserted-by":"publisher","first-page":"102656","DOI":"10.1016\/j.media.2022.102656","volume":"83","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Zhang, J., Tian, B., Lukasiewicz, T., Xu, Z.: Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med. Image Anal. 83, 102656 (2023)","journal-title":"Med. Image Anal."},{"key":"47_CR21","doi-asserted-by":"publisher","unstructured":"Zhang, Y., et al.: Modality-aware mutual learning for multi-modal medical image segmentation. In: de\u00a0Bruijne, M., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021, pp. 589\u2013599. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_56","DOI":"10.1007\/978-3-030-87193-2_56"},{"key":"47_CR22","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3\u20134","author":"T Zhou","year":"2019","unstructured":"Zhou, T., Ruan, S., Canu, S.: A review: deep learning for medical image segmentation using multi-modality fusion. Array 3\u20134, 100004 (2019)","journal-title":"Array"},{"key":"47_CR23","doi-asserted-by":"publisher","unstructured":"Zhou, X., Sun, Y., Deng, M., Chu, W.C.W., Dou, Q.: Robust semi-supervised multimodal medical image segmentation via cross modality collaboration. In: Linguraru, M.G., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 57\u201367. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72378-0_6","DOI":"10.1007\/978-3-031-72378-0_6"}],"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\/978-3-032-04984-1_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T16:25:06Z","timestamp":1758299106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04984-1_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049834","9783032049841"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04984-1_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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\u00a0are 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"}}]}}