{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T07:48:06Z","timestamp":1777967286995,"version":"3.51.4"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164392","type":"print"},{"value":"9783031164408","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16440-8_2","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"14-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Efficient Biomedical Instance Segmentation via\u00a0Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Xiaoyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/978-3-319-66179-7_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"EMA Anas","year":"2017","unstructured":"Anas, E.M.A., et al.: Clinical target-volume delineation in prostate brachytherapy using residual neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 365\u2013373. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_42"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Avelar, P.H., Tavares, A.R., da Silveira, T.L., Jung, C.R., Lamb, L.C.: Superpixel image classification with graph attention networks. In: 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 203\u2013209. IEEE (2020)","DOI":"10.1109\/SIBGRAPI51738.2020.00035"},{"issue":"2","key":"2_CR3","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1038\/nmeth.4151","volume":"14","author":"T Beier","year":"2017","unstructured":"Beier, T., et al.: Multicut brings automated neurite segmentation closer to human performance. Nat. Methods 14(2), 101\u2013102 (2017)","journal-title":"Nat. Methods"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, X., Yu, L., Heng, P.A.: DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2487\u20132496 (2016)","DOI":"10.1109\/CVPR.2016.273"},{"key":"2_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/978-3-030-32239-7_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Chen","year":"2019","unstructured":"Chen, L., Strauch, M., Merhof, D.: Instance segmentation of biomedical images with an object-aware embedding learned with local constraints. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 451\u2013459. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_50"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5008\u20135017 (2021)","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"2_CR7","unstructured":"CREMI: Miccal challenge on circuit reconstruction from electron microscopy images (2016). https:\/\/cremi.org\/"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-030-32245-8_10","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Dong","year":"2019","unstructured":"Dong, M., et al.: Instance segmentation from volumetric biomedical images without voxel-wise labeling. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 83\u201391. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_10"},{"issue":"7","key":"2_CR9","doi-asserted-by":"publisher","first-page":"1669","DOI":"10.1109\/TPAMI.2018.2835450","volume":"41","author":"J Funke","year":"2018","unstructured":"Funke, J., et al.: Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1669\u20131680 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"2_CR11","unstructured":"Hinton, G., Vinyals, O., Dean, J., et al.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2(7) (2015)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Hu, B., Zhou, S., Xiong, Z., Wu, F.: Cross-resolution distillation for efficient 3D medical image registration. IEEE Trans. Circuits Syst. Video Technol. (2022)","DOI":"10.1109\/TCSVT.2022.3178178"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Semi-supervised neuron segmentation via reinforced consistency learning. IEEE Trans. Med. Imaging (2022)","DOI":"10.1109\/TMI.2022.3176050"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Huang, W., Deng, S., Chen, C., Fu, X., Xiong, Z.: Learning to model pixel-embedded affinity for homogeneous instance segmentation. In: Proceedings of AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i1.19984"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Ke, T.W., Hwang, J.J., Liu, Z., Yu, S.X.: Adaptive affinity fields for semantic segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 587\u2013602 (2018)","DOI":"10.1007\/978-3-030-01246-5_36"},{"key":"2_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Kulikov, V., Lempitsky, V.: Instance segmentation of biological images using harmonic embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3843\u20133851 (2020)","DOI":"10.1109\/CVPR42600.2020.00390"},{"issue":"12","key":"2_CR18","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1109\/TMI.2021.3097826","volume":"40","author":"K Lee","year":"2021","unstructured":"Lee, K., Lu, R., Luther, K., Seung, H.S.: Learning and segmenting dense voxel embeddings for 3D neuron reconstruction. IEEE Trans. Med. Imaging 40(12), 3801\u20133811 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, C., Liu, X., Huang, W., Zhang, Y., Xiong, Z.: Advanced deep networks for 3D mitochondria instance segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761477"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Liu, X., Huang, W., Zhang, Y., Xiong, Z.: Biological instance segmentation with a superpixel-guided graph. In: IJCAI (2022)","DOI":"10.24963\/ijcai.2022\/169"},{"key":"2_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/978-3-030-87237-3_42","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Liu","year":"2021","unstructured":"Liu, X., et al.: Learning neuron stitching for connectomics. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 435\u2013444. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87237-3_42"},{"key":"2_CR22","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-540-45167-9_14","volume-title":"Learning Theory and Kernel Machines","author":"M Meil\u0103","year":"2003","unstructured":"Meil\u0103, M.: Comparing clusterings by the variation of information. In: Sch\u00f6lkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 173\u2013187. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45167-9_14"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patrec.2015.10.013","volume":"81","author":"M Minervini","year":"2016","unstructured":"Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recogn. Lett. 81, 80\u201389 (2016)","journal-title":"Pattern Recogn. Lett."},{"key":"2_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00934-2_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Payer","year":"2018","unstructured":"Payer, C., \u0160tern, D., Neff, T., Bischof, H., Urschler, M.: Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_1"},{"issue":"12","key":"2_CR25","doi-asserted-by":"publisher","first-page":"3820","DOI":"10.1109\/TMI.2021.3098703","volume":"40","author":"D Qin","year":"2021","unstructured":"Qin, D., et al.: Efficient medical image segmentation based on knowledge distillation. IEEE Trans. Med. Imaging 40(12), 3820\u20133831 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"336","key":"2_CR26","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846\u2013850 (1971)","journal-title":"J. Am. Stat. Assoc."},{"key":"2_CR27","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"2_CR28","unstructured":"Scharr, H., Minervini, M., Fischbach, A., Tsaftaris, S.A.: Annotated image datasets of rosette plants. In: ECCV (2014)"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Wolf, S., et al.: The mutex watershed and its objective: efficient, parameter-free graph partitioning. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/TPAMI.2020.2980827"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Fu, X., Huang, J., Cheng, Z., Xiong, Z.: Space-time distillation for video super-resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2113\u20132122 (2021)","DOI":"10.1109\/CVPR46437.2021.00215"},{"key":"2_CR32","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16440-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T18:06:33Z","timestamp":1711562793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16440-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164392","9783031164408"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16440-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2022\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}