{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:59:54Z","timestamp":1743112794142,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030306441"},{"type":"electronic","value":"9783030306458"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-30645-8_61","type":"book-chapter","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:08:15Z","timestamp":1567584495000},"page":"672-682","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["On Generative Modeling of Cell Shape Using 3D GANs"],"prefix":"10.1007","author":[{"given":"David","family":"Wiesner","sequence":"first","affiliation":[]},{"given":"Tereza","family":"Ne\u010dasov\u00e1","sequence":"additional","affiliation":[]},{"given":"David","family":"Svoboda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,2]]},"reference":[{"key":"61_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 214\u2013223. PMLR, International Convention Centre, Sydney, Australia, 06\u201311 August 2017"},{"key":"61_CR2","unstructured":"Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)"},{"issue":"17","key":"61_CR3","doi-asserted-by":"publisher","first-page":"3805","DOI":"10.1242\/jcs.118349","volume":"126","author":"DL Coutu","year":"2013","unstructured":"Coutu, D.L., Schroeder, T.: Probing cellular processes by long-term live imaging-historic problems and current solutions. J. Cell Sci. 126(17), 3805\u20133815 (2013)","journal-title":"J. Cell Sci."},{"key":"61_CR4","doi-asserted-by":"crossref","unstructured":"Goldsborough, P., Pawlowski, N., Caicedo, J.C., Singh, S., Carpenter, A.: CytoGAN: Generative modeling of cell images. bioRxiv, p. 227645 (2017)","DOI":"10.1101\/227645"},{"key":"61_CR5","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672\u20132680. Curran Associates, Inc. (2014)"},{"key":"61_CR6","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein GANs. In: Advances in Neural Information Processing Systems vol. 30, pp. 5767\u20135777. Curran Associates, Inc. (2017)"},{"key":"61_CR7","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 609\u2013616. ACM, New York (2009)","DOI":"10.1145\/1553374.1553453"},{"key":"61_CR8","unstructured":"Mescheder, L., Geiger, A., Nowozin, S.: Which training methods for GANs do actually converge? In: International Conference on Machine learning (ICML) (2018)"},{"key":"61_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/978-3-030-11024-6_28","volume-title":"Computer Vision \u2013 ECCV 2018 Workshops","author":"T Ne\u010dasov\u00e1","year":"2019","unstructured":"Ne\u010dasov\u00e1, T., Svoboda, D.: Visual and quantitative comparison of real and simulated biomedical image data. In: Leal-Taix\u00e9, L., Roth, S. (eds.) Computer Vision \u2013 ECCV 2018 Workshops. LNCS, vol. 11134, pp. 385\u2013394. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11024-6_28"},{"issue":"1","key":"61_CR10","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transact. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Transact. Syst. Man Cybern."},{"key":"61_CR11","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"61_CR12","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in neural information processing systems, pp. 2234\u20132242 (2016)"},{"key":"61_CR13","unstructured":"Smith, E., Meger, D.: Improved adversarial systems for 3D object generation and reconstruction. arXiv preprint arXiv:1707.09557 (2017)"},{"issue":"1","key":"61_CR14","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/TMI.2016.2606545","volume":"36","author":"D Svoboda","year":"2017","unstructured":"Svoboda, D., Ulman, V.: MitoGen: a framework for generating 3D synthetic time-lapse sequences of cell populations in fluorescence microscopy. IEEE Transact. Med. Imaging 36(1), 310\u2013321 (2017)","journal-title":"IEEE Transact. Med. Imaging"},{"issue":"6","key":"61_CR15","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1002\/cyto.a.20714","volume":"75","author":"D Svoboda","year":"2009","unstructured":"Svoboda, D., Kozubek, M., Stejskal, S.: Generation of digital phantoms of cell nuclei and simulation of image formation in 3D image cytometry. Cytometry Part A J. Int. Soc. Adv. Cytometry 75(6), 494\u2013509 (2009)","journal-title":"Cytometry Part A J. Int. Soc. Adv. Cytometry"},{"issue":"12","key":"61_CR16","doi-asserted-by":"publisher","first-page":"1057","DOI":"10.1002\/cyto.a.23031","volume":"89","author":"V Ulman","year":"2016","unstructured":"Ulman, V., Svoboda, D., Nykter, M., Kozubek, M., Ruusuvuori, P.: Virtual cell imaging: a review on simulation methods employed in image cytometry. Cytometry Part A 89(12), 1057\u20131072 (2016)","journal-title":"Cytometry Part A"},{"issue":"12","key":"61_CR17","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(12), 1141 (2017)","journal-title":"Nat. Methods"},{"key":"61_CR18","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, vol. 29, pp. 82\u201390. Curran Associates, Inc. (2016)"},{"key":"61_CR19","unstructured":"Wu, Z., et al.: 3D ShapeNets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1912\u20131920 (2015)"},{"key":"61_CR20","doi-asserted-by":"crossref","unstructured":"Xiong, W., Luo, W., Ma, L., Liu, W., Luo, J.: Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2364\u20132373 (2018)","DOI":"10.1109\/CVPR.2018.00251"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30645-8_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:08:59Z","timestamp":1693786139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30645-8_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030306441","9783030306458"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30645-8_61","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"2 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.unitn.it\/iciap2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"207","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":"117","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":"57% - 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":"2.6","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}