{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T19:59:06Z","timestamp":1767988746479,"version":"3.49.0"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721137","type":"print"},{"value":"9783031721144","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\/978-3-031-72114-4_28","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:01:43Z","timestamp":1727874103000},"page":"286-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Few-Shot 3D Volumetric Segmentation with\u00a0Multi-surrogate Fusion"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6677-2017","authenticated-orcid":false,"given":"Meng","family":"Zheng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6110-6437","authenticated-orcid":false,"given":"Benjamin","family":"Planche","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4344-4501","authenticated-orcid":false,"given":"Zhongpai","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Terrence","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5064-7775","authenticated-orcid":false,"given":"Richard J.","family":"Radke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9774-7770","authenticated-orcid":false,"given":"Ziyan","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"28_CR1","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Ding, H., Sun, C., Tang, H., Cai, D., Yan, Y.: Few-shot medical image segmentation with cycle-resemblance attention. In: WACV, pp. 2487\u20132496 (2023)","DOI":"10.1109\/WACV56688.2023.00252"},{"key":"28_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102834","volume":"87","author":"Y Feng","year":"2023","unstructured":"Feng, Y., Wang, Y., Li, H., Qu, M., Yang, J.: Learning what and where to segment: a new perspective on medical image few-shot segmentation. Med. Image Anal. 87, 102834 (2023)","journal-title":"Med. Image Anal."},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et\u00a0al.: UNETR: transformers for 3D medical image segmentation. In: WACV (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"28_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Hong, S., Cho, S., Nam, J., Lin, S., Kim, S.: Cost aggregation with 4D convolutional swin transformer for few-shot segmentation. In: ECCV (2022)","DOI":"10.1007\/978-3-031-19818-2_7"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, P., Zhang, C., Yu, G., Mu, Y., Snoek, C.G.M.: Attention-based multi-context guiding for few-shot semantic segmentation. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33018441"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Huang, S., Xu, T., Shen, N., Mu, F., Li, J.: Rethinking few-shot medical segmentation: a vector quantization view. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00300"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Kavur, A.E., Gezer, N.S., Bar\u0131\u015f, M., et\u00a0al.: CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. (2021). http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841520303145","DOI":"10.1016\/j.media.2020.101950"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Kim, S., An, S., Chikontwe, P., Park, S.H.: Bidirectional RNN-based few shot learning for 3D medical image segmentation. In: AAAI (2021)","DOI":"10.1609\/aaai.v35i3.16275"},{"key":"28_CR11","unstructured":"Landman, B., Xu, Z., Igelsias, J., Styner, M., Langerak, T., Klein, A.: MICCAI multi-atlas labeling beyond the cranial vault-workshop and challenge (2015)"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Lang, C., Cheng, G., Tu, B., Han, J.: Learning what not to segment: a new perspective on few-shot segmentation. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.00789"},{"key":"28_CR13","doi-asserted-by":"crossref","unstructured":"Lei, W., et\u00a0al.: One-shot weakly-supervised segmentation in 3D medical images. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3294975"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Li, G., Jampani, V., Sevilla-Lara, L., Sun, D., Kim, J., Kim, J.: Adaptive prototype learning and allocation for few-shot segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Lin, T., Maire, M., Belongie, S.J., et\u00a0al.: Microsoft COCO: common objects in context. CoRR (2014)","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., Chen, Y., Cheng, K.T., Chen, H.: Few shot medical image segmentation with cross attention transformer (2023)","DOI":"10.1007\/978-3-031-43895-0_22"},{"key":"28_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhang, C., Lin, G., Liu, F.: Crnet: cross-reference networks for few-shot segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00422"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Min, J., Kang, D., Cho, M.: Hypercorrelation squeeze for few-shot segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00686"},{"key":"28_CR19","doi-asserted-by":"crossref","unstructured":"Niu, Y., Li, Z., Li, S.: Cross attention with transformer for few-shot medical image segmentation. In: International Conference on Information Technology in Medicine and Education (2022)","DOI":"10.1109\/ITME56794.2022.00137"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58526-6_45"},{"issue":"9","key":"28_CR21","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1109\/TMI.2023.3258069","volume":"42","author":"P Pandey","year":"2023","unstructured":"Pandey, P., Chasmai, M., Sur, T., Lall, B.: Robust prototypical few-shot organ segmentation with regularized neural-odes. IEEE Trans. Med. Imaging 42(9), 2490\u20132501 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"28_CR22","unstructured":"Pont-Tuset, J., Perazzi, F., Caelles, S., Arbel\u00e1ez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 DAVIS challenge on video object segmentation. arXiv:1704.00675 (2017)"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"28_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101587","volume":"59","author":"AG Roy","year":"2020","unstructured":"Roy, A.G., Siddiqui, S., P\u00f6lsterl, S., Navab, N., Wachinger, C.: \u2018Squeeze & excite\u2019 guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)","journal-title":"Med. Image Anal."},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: One-shot learning for semantic segmentation. In: BMVC (2017)","DOI":"10.5244\/C.31.167"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Shi, X., Wei, D., Zhang, Y., Lu, D., Ning, M., Chen, J., Ma, K., Zheng, Y.: Dense cross-query-and-support attention weighted mask aggregation for few-shot segmentation. In: ECCV (2022)","DOI":"10.1007\/978-3-031-20044-1_9"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00389"},{"key":"28_CR28","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et\u00a0al.: Attention is all you need. In: NeurIPS (2017)"},{"key":"28_CR29","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, X., Hu, Y., Yang, Y., Cao, X., Zhen, X.: Few-shot semantic segmentation with democratic attention networks. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58601-0_43"},{"key":"28_CR30","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: few-shot image semantic segmentation with prototype alignment. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"28_CR31","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhou, Q., Zheng, G.: Few-shot medical image segmentation regularized with self-reference and contrastive learning. In: MICCAI (2022)","DOI":"10.1007\/978-3-031-16440-8_49"},{"key":"28_CR32","doi-asserted-by":"crossref","unstructured":"Wang, X., Han, S., Chen, Y., Gao, D., Vasconcelos, N.: Volumetric attention for 3D medical image segmentation and detection. In: MICCAI (2019)","DOI":"10.1007\/978-3-030-32226-7_20"},{"key":"28_CR33","doi-asserted-by":"crossref","unstructured":"Xie, Y., Zhang, J., Shen, C., Xia, Y.: CoTr: efficiently bridging CNN and transformer for 3D medical image segmentation. In: MICCAI (2021)","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"28_CR34","doi-asserted-by":"crossref","unstructured":"Xu, N., et al.: Youtube-VOS: a large-scale video object segmentation benchmark. arXiv abs\/1809.03327 (2018)","DOI":"10.1007\/978-3-030-01228-1_36"},{"key":"28_CR35","doi-asserted-by":"crossref","unstructured":"Yu, Q., Dang, K., Tajbakhsh, N., Terzopoulos, D., Ding, X.: A location-sensitive local prototype network for few-shot medical image segmentation. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI) (2021)","DOI":"10.1109\/ISBI48211.2021.9434008"},{"key":"28_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, B., Xiao, J., Qin, T.: Self-guided and cross-guided learning for few-shot segmentation. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00821"},{"key":"28_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Guo, J., Wu, Q., Yao, R.: Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00968"},{"key":"28_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., Shen, C.: Canet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00536"},{"issue":"9","key":"28_CR39","doi-asserted-by":"publisher","first-page":"3855","DOI":"10.1109\/TCYB.2020.2992433","volume":"50","author":"X Zhang","year":"2020","unstructured":"Zhang, X., Wei, Y., Yang, Y., Huang, T.S.: SG-one: similarity guidance network for one-shot semantic segmentation. IEEE Trans. Cybern. 50(9), 3855\u20133865 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"28_CR40","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, S., Xin, T., Zhang, H.: Few-shot medical image segmentation via a region-enhanced prototypical transformer. In: MICCAI 2023 (2023)","DOI":"10.1007\/978-3-031-43901-8_26"}],"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\/978-3-031-72114-4_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:04:52Z","timestamp":1727874292000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721137","9783031721144"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72114-4_28","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"}}]}}