{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:25:13Z","timestamp":1778084713149,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030871925","type":"print"},{"value":"9783030871932","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87193-2_16","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:25:10Z","timestamp":1632342310000},"page":"164-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale"],"prefix":"10.1007","author":[{"given":"Zudi","family":"Lin","sequence":"first","affiliation":[]},{"given":"Donglai","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Mariela D.","family":"Petkova","sequence":"additional","affiliation":[]},{"given":"Yuelong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Zergham","family":"Ahmed","sequence":"additional","affiliation":[]},{"given":"Krishna Swaroop","family":"K","sequence":"additional","affiliation":[]},{"given":"Silin","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Nils","family":"Wendt","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Boulanger-Weill","sequence":"additional","affiliation":[]},{"given":"Xueying","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Nagaraju","family":"Dhanyasi","sequence":"additional","affiliation":[]},{"given":"Ignacio","family":"Arganda-Carreras","sequence":"additional","affiliation":[]},{"given":"Florian","family":"Engert","sequence":"additional","affiliation":[]},{"given":"Jeff","family":"Lichtman","sequence":"additional","affiliation":[]},{"given":"Hanspeter","family":"Pfister","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"e19766","DOI":"10.7554\/eLife.19766","volume":"5","author":"F Alwes","year":"2016","unstructured":"Alwes, F., Enjolras, C., Averof, M.: Live imaging reveals the progenitors and cell dynamics of limb regeneration. Elife 5, e19766 (2016)","journal-title":"Elife"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.3389\/fncir.2018.00088","volume":"12","author":"DR Berger","year":"2018","unstructured":"Berger, D.R., Seung, H.S., Lichtman, J.W.: Vast (volume annotation and segmentation tool): efficient manual and semi-automatic labeling of large 3D image stacks. Front. Neural Circ. 12, 88 (2018)","journal-title":"Front. Neural Circ."},{"key":"16_CR3","unstructured":"Bottou, L.: Stochastic gradient learning in neural networks. In: Proceedings of Neuro-N\u0131mes (1991)"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1038\/s41592-019-0612-7","volume":"16","author":"JC Caicedo","year":"2019","unstructured":"Caicedo, J.C., et al.: Nucleus segmentation across imaging experiments: the 2018 data science bowl. Nat. Methods 16, 1247\u20131253 (2019)","journal-title":"Nat. Methods"},{"key":"16_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"16_CR6","unstructured":"Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NeurIPS (2012)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1109\/TPAMI.2008.173","volume":"31","author":"J Cousty","year":"2008","unstructured":"Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: minimum spanning forests and the drop of water principle. TPAMI 31, 1362\u20131374 (2008)","journal-title":"TPAMI"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Dyer, E.L., et al.: Quantifying mesoscale neuroanatomy using x-ray microtomography. Eneuro (2017)","DOI":"10.1523\/ENEURO.0195-17.2017"},{"key":"16_CR10","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":"16_CR11","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00065"},{"key":"16_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/978-3-030-00934-2_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"L Heinrich","year":"2018","unstructured":"Heinrich, L., Funke, J., Pape, C., Nunez-Iglesias, J., Saalfeld, S.: Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 317\u2013325. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_36"},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1038\/s41592-018-0049-4","volume":"15","author":"M Januszewski","year":"2018","unstructured":"Januszewski, M., et al.: High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605\u2013610 (2018)","journal-title":"Nat. Methods"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.cell.2015.06.054","volume":"162","author":"N Kasthuri","year":"2015","unstructured":"Kasthuri, N., et al.: Saturated reconstruction of a volume of neocortex. Cell 162, 648\u2013661 (2015)","journal-title":"Cell"},{"key":"16_CR15","first-page":"829","volume":"37","author":"N Krasowski","year":"2017","unstructured":"Krasowski, N., Beier, T., Knott, G., K\u00f6the, U., Hamprecht, F.A., Kreshuk, A.: Neuron segmentation with high-level biological priors. TMI 37, 829\u2013839 (2017)","journal-title":"TMI"},{"key":"16_CR16","unstructured":"Lee, K., Zung, J., Li, P., Jain, V., Seung, H.S.: Superhuman accuracy on the SNEMI3D connectomics challenge. arXiv:1706.00120 (2017)"},{"key":"16_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"TY Lin","year":"2014","unstructured":"Lin, T.Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"16_CR18","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.stemcr.2014.01.010","volume":"2","author":"X Lou","year":"2014","unstructured":"Lou, X., Kang, M., Xenopoulos, P., Munoz-Descalzo, S., Hadjantonakis, A.K.: A rapid and efficient 2d\/3d nuclear segmentation method for analysis of early mouse embryo and stem cell image data. Stem Cell Rep. 2, 382\u2013397 (2014)","journal-title":"Stem Cell Rep."},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/MSP.2012.2204190","volume":"29","author":"E Meijering","year":"2012","unstructured":"Meijering, E.: Cell segmentation: 50 years down the road. Signal Process. Mag. 29, 140\u2013145 (2012)","journal-title":"Signal Process. Mag."},{"key":"16_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"HTT Nhu","year":"2017","unstructured":"Nhu, H.T.T., Drigo, R.A.E., Berggren, P.O., Boudier, T.: A novel toolbox to investigate tissue spatial organization applied to the study of the islets of langerhans. Sci. Rep. 7, 1\u201312 (2017)","journal-title":"Sci. Rep."},{"key":"16_CR21","unstructured":"Petkova, M.: Correlative Light and Electron Microscopy in an Intact Larval Zebrafish. Ph.D. thesis (2020)"},{"key":"16_CR22","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 \u2013 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. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3389\/fnana.2019.00081","volume":"13","author":"B Ruszczycki","year":"2019","unstructured":"Ruszczycki, B., et al.: Three-dimensional segmentation and reconstruction of neuronal nuclei in confocal microscopic images. Front. Neuroanatomy 13, 81 (2019)","journal-title":"Front. Neuroanatomy"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Shapson-Coe, A., et al.: A connectomic study of a petascale fragment of human cerebral cortex. bioRxiv (2021)","DOI":"10.1101\/2021.05.29.446289"},{"key":"16_CR25","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.devcel.2015.12.028","volume":"36","author":"J Stegmaier","year":"2016","unstructured":"Stegmaier, J., et al.: Real-time three-dimensional cell segmentation in large-scale microscopy data of developing embryos. Dev. Cell 36, 225\u2013240 (2016)","journal-title":"Dev. Cell"},{"key":"16_CR26","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","volume":"18","author":"C Stringer","year":"2021","unstructured":"Stringer, C., Wang, T., Michaelos, M., Pachitariu, M.: Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100\u2013106 (2021)","journal-title":"Nat. Methods"},{"key":"16_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41540-020-00152-8","volume":"6","author":"Y Tokuoka","year":"2020","unstructured":"Tokuoka, Y., et al.: 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis. NPJ Syst. Biol. Appl. 6, 1\u201312 (2020)","journal-title":"NPJ Syst. Biol. Appl."},{"key":"16_CR28","doi-asserted-by":"publisher","first-page":"e1004970","DOI":"10.1371\/journal.pcbi.1004970","volume":"12","author":"Y Toyoshima","year":"2016","unstructured":"Toyoshima, Y., et al.: Accurate automatic detection of densely distributed cell nuclei in 3D space. PLoS Comput. Biol. 12, e1004970 (2016)","journal-title":"PLoS Comput. Biol."},{"key":"16_CR29","unstructured":"Turaga, S.C., Briggman, K.L., Helmstaedter, M., Denk, W., Seung, H.S.: Maximin affinity learning of image segmentation. In: NeurIPS (2009)"},{"key":"16_CR30","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.1038\/nmeth.4473","volume":"14","author":"V Ulman","year":"2017","unstructured":"Ulman, V., et al.: An objective comparison of cell-tracking algorithms. Nat. Methods 14, 1141\u20131152 (2017)","journal-title":"Nat. Methods"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"van der Walt, S., et al.: The scikit-image contributors: scikit-image: image processing in Python. PeerJ (2014)","DOI":"10.7287\/peerj.preprints.336v2"},{"key":"16_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-030-59722-1_7","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"D Wei","year":"2020","unstructured":"Wei, D., et al.: MitoEM dataset: large-scale 3D mitochondria instance segmentation from EM images. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 66\u201376. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_7"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Weigert, M., Schmidt, U., Haase, R., Sugawara, K., Myers, G.: Star-convex polyhedra for 3d object detection and segmentation in microscopy. In: WACV (2020)","DOI":"10.1109\/WACV45572.2020.9093435"},{"key":"16_CR34","unstructured":"Zhou, P., Feng, J., Ma, C., Xiong, C., HOI, S., et al.: Towards theoretically understanding why sgd generalizes better than adam in deep learning. arXiv preprint arXiv:2010.05627 (2020)"},{"key":"16_CR35","unstructured":"Zlateski, A., Seung, H.S.: Image segmentation by size-dependent single linkage clustering of a watershed basin graph. arXiv:1505.00249 (2015)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87193-2_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T14:11:24Z","timestamp":1643379084000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87193-2_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871925","9783030871932"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87193-2_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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 CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}