{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:21:35Z","timestamp":1743099695096,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819709021"},{"type":"electronic","value":"9789819709038"}],"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\/978-981-97-0903-8_3","type":"book-chapter","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:02:50Z","timestamp":1709204570000},"page":"23-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Guided Local Prototype Network for Few-Shot Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Pengrui","family":"Teng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhu","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi-Jie","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changan","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Wang, G., et al.: DeepiGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1559\u20131572 (2018). Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE\u00a02016, LNCS, vol. 9999, pp.\u00a01\u201313. Springer, Heidelberg (2016)","DOI":"10.1109\/TPAMI.2018.2840695"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"2165","DOI":"10.1007\/s00259-010-1423-3","volume":"37","author":"H Zaidi","year":"2010","unstructured":"Zaidi, H., El Naqa, I.: PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur. J. Nucl. Med. Mol. Imaging 37, 2165\u20132187 (2010)","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"3_CR3","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 \u2014 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, Part III. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"issue":"2","key":"3_CR5","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., et al.: NnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"3_CR6","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., et al.: \u2018Squeeze & excite\u2019 guided few-shot segmentation of volumetric images. Med. Image Anal. 59, 101587 (2020)","journal-title":"Med. Image Anal."},{"key":"3_CR7","doi-asserted-by":"publisher","unstructured":"Ouyang, C., Biffi, C., Chen, C., et al.: Self-supervision with superpixels: training few-shot medical image segmentation without annotation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision\u2013ECCV 2020, Part XXIX, pp. 762\u2013780. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_45","DOI":"10.1007\/978-3-030-58526-6_45"},{"key":"3_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102385","volume":"78","author":"S Hansen","year":"2022","unstructured":"Hansen, S., Gautam, S., Jenssen, R., et al.: Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Med. Image Anal. 78, 102385 (2022)","journal-title":"Med. Image Anal."},{"key":"3_CR9","unstructured":"Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"3_CR10","unstructured":"Iqbal, E., Safarov, S., Bang, S.: MSANet: multi-similarity and attention guidance for boosting few-shot segmentation. arXiv preprint arXiv:2206.09667 (2022)"},{"key":"3_CR11","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., et al.: 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":"3_CR12","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., et al.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"3_CR13","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning, PMLR, pp. 1126\u20131135 (2017)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Jamal, M.A., Qi, G.J.: Task agnostic meta-learning for few-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11719\u201311727 (2019)","DOI":"10.1109\/CVPR.2019.01199"},{"key":"3_CR15","unstructured":"Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning. In: International Conference on Learning Representations (2016)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fu, Y., Wang, Y.X., et al.: Image deformation meta-networks for one-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8680\u20138689 (2019)","DOI":"10.1109\/CVPR.2019.00888"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fu, Y., Chen, K., et al.: Image block augmentation for one-shot learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 3379\u20133386 (2019)","DOI":"10.1609\/aaai.v33i01.33013379"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, A., Balakrishnan, G., Durand, F., et al.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543\u20138553 (2019)","DOI":"10.1109\/CVPR.2019.00874"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., et al.: PANet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9197\u20139206 (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"3_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105067","volume":"140","author":"L Sun","year":"2022","unstructured":"Sun, L., Li, C., Ding, X., et al.: Few-shot medical image segmentation using a global correlation network with discriminative embedding. Comput. Biol. Med. 140, 105067 (2022)","journal-title":"Comput. Biol. Med."},{"issue":"10","key":"3_CR21","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TMI.2021.3060551","volume":"40","author":"R Feng","year":"2021","unstructured":"Feng, R., Zheng, X., Gao, T., et al.: Interactive few-shot learning: limited supervision, better medical image segmentation. IEEE Trans. Med. Imaging 40(10), 2575\u20132588 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"Wu, H., Xiao, F., Liang, C.: Dual contrastive learning with anatomical auxiliary supervision for few-shot medical image segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13680, pp. 417\u2013434. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20044-1_24","DOI":"10.1007\/978-3-031-20044-1_24"},{"key":"3_CR23","unstructured":"Shen, Q., Li, Y., Jin, J., et al.: Q-net: query-informed few-shot medical image segmentation. arXiv preprint arXiv:2208.11451 (2022)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, X., Sun, S., et al.: Recurrent mask refinement for few-shot medical image segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3918\u20133928 (2021)","DOI":"10.1109\/ICCV48922.2021.00389"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: 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"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Kavur, A.E., Gezer, N.S., Bar\u0131\u015f, M., Aslan, S., Conze, P.-H., et al.: CHAOS challenge - combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)","DOI":"10.1016\/j.media.2020.101950"},{"key":"3_CR27","unstructured":"Bennett, L., Xu, Z., Eugenio, I.J., Martin, S., Robin, L.T., Arno, K.: MICCAI multi-atlas labeling beyond the cranial vault\u2013workshop and challenge (2015)"},{"key":"3_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-319-46723-8_67","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 581\u2013588. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_67"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0903-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T11:12:20Z","timestamp":1709205140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0903-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819709021","9789819709038"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0903-8_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 March 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanning","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icai.org.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}