{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T14:04:34Z","timestamp":1775916274695,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031090363","type":"print"},{"value":"9783031090370","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\/978-3-031-09037-0_20","type":"book-chapter","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T04:26:16Z","timestamp":1654057576000},"page":"236-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Cell Segmentation in\u00a0Fluorescence Microscopy Images via\u00a0Self-supervised Learning"],"prefix":"10.1007","author":[{"given":"Carola","family":"Krug","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karl","family":"Rohr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"issue":"11","key":"20_CR1","doi-asserted-by":"publisher","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","volume":"34","author":"R Achanta","year":"2012","unstructured":"Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274\u20132282 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. arXiv:2104.14294 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"20_CR3","unstructured":"Cho, J.H., et al.: PiCIE: unsupervised semantic segmentation using invariance and equivariance in clustering. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16794\u201316804 (2021)"},{"issue":"1","key":"20_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-54244-5","volume":"9","author":"KW Dunn","year":"2019","unstructured":"Dunn, K.W., et al.: DeepSynth: three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data. Sci. Rep. 9(1), 1\u201315 (2019)","journal-title":"Sci. Rep."},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Fujii, H., et al.: X-net with different loss functions for cell image segmentation. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3793\u20133800 (2021)","DOI":"10.1109\/CVPRW53098.2021.00420"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"214","DOI":"10.5201\/ipol.2012.g-cv","volume":"2","author":"P Getreuer","year":"2012","unstructured":"Getreuer, P.: Chan-Vese segmentation. Image Process. On Line 2, 214\u2013224 (2012)","journal-title":"Image Process. On Line"},{"key":"20_CR7","unstructured":"Haq, M.M., Huang, J.: Adversarial domain adaptation for cell segmentation. In: Proceedings of Medical Imaging with Deep Learning, pp. 277\u2013287. PMLR (2020)"},{"key":"20_CR8","unstructured":"Horlava, N., et al.: A comparative study of semi-and self-supervised semantic segmentation of biomedical microscopy data. arXiv:2011.08076 (2020)"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Ji, X., et al.: Invariant information clustering for unsupervised image classification and segmentation. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 9865\u20139874 (2019)","DOI":"10.1109\/ICCV.2019.00996"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","volume":"43","author":"L Jing","year":"2020","unstructured":"Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 43, 4037\u20134058 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Larsson, G., et al.: Colorization as a proxy task for visual understanding. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 6874\u20136883 (2017)","DOI":"10.1109\/CVPR.2017.96"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Unsupervised instance segmentation in microscopy images via panoptic domain adaptation and task re-weighting. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4243\u20134252 (2020)","DOI":"10.1109\/CVPR42600.2020.00430"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Q., et al.: GAN based unsupervised segmentation: should we match the exact number of objects. In: Proceedings of Medical Imaging 2021: Image Processing, vol. 11596. International Society for Optics and Photonics (2021)","DOI":"10.1117\/12.2580671"},{"issue":"11","key":"20_CR14","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1093\/bioinformatics\/btu080","volume":"30","author":"M Ma\u0161ka","year":"2014","unstructured":"Ma\u0161ka, M., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609\u20131617 (2014)","journal-title":"Bioinformatics"},{"issue":"1","key":"20_CR15","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 Trans. Syst. Man Cybern. 9(1), 62\u201366 (1979)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Robitaille, M.C., et al.: A self-supervised machine learning approach for objective live cell segmentation and analysis. bioRxiv:2021.01.07.425773 (2021)","DOI":"10.1101\/2021.01.07.425773"},{"key":"20_CR17","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":"20_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-3-030-59722-1_38","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"M Sahasrabudhe","year":"2020","unstructured":"Sahasrabudhe, M., et al.: Self-supervised nuclei segmentation in histopathological images using attention. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 393\u2013402. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_38"},{"issue":"12","key":"20_CR19","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\u20131152 (2017)","journal-title":"Nat. Methods"},{"issue":"11","key":"20_CR20","doi-asserted-by":"publisher","first-page":"e1005177","DOI":"10.1371\/journal.pcbi.1005177","volume":"12","author":"DA Van Valen","year":"2016","unstructured":"Van Valen, D.A., et al.: Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12(11), e1005177 (2016)","journal-title":"PLoS Comput. Biol."},{"issue":"06","key":"20_CR21","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1109\/34.87344","volume":"13","author":"L Vincent","year":"1991","unstructured":"Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE TPAMI 13(06), 583\u2013598 (1991)","journal-title":"IEEE TPAMI"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Differential treatment for stuff and things: a simple unsupervised domain adaptation method for semantic segmentation. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12635\u201312644 (2020)","DOI":"10.1109\/CVPR42600.2020.01265"},{"key":"20_CR23","unstructured":"Wolf, S., et al.: Inpainting networks learn to separate cells in microscopy images. In: Proceedings of BMVC (2020)"},{"key":"20_CR24","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.media.2019.04.011","volume":"56","author":"T Wollmann","year":"2019","unstructured":"Wollmann, T., et al.: GRUU-Net: integrated convolutional and gated recurrent neural network for cell segmentation. Med. Image Anal. 56, 68\u201379 (2019)","journal-title":"Med. Image Anal."},{"key":"20_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/978-3-030-59722-1_33","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"X Xie","year":"2020","unstructured":"Xie, X., Chen, J., Li, Y., Shen, L., Ma, K., Zheng, Y.: Instance-aware self-supervised learning for nuclei segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 341\u2013350. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_33"},{"key":"20_CR26","doi-asserted-by":"publisher","first-page":"101840","DOI":"10.1016\/j.media.2020.101840","volume":"67","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., et al.: Models genesis. Med. Image Anal. 67, 101840 (2021)","journal-title":"Med. Image Anal."},{"key":"20_CR27","doi-asserted-by":"publisher","first-page":"101746","DOI":"10.1016\/j.media.2020.101746","volume":"64","author":"J Zhu","year":"2020","unstructured":"Zhu, J., et al.: Rubik\u2019s cube+: a self-supervised feature learning framework for 3D medical image analysis. Med. Image Anal. 64, 101746 (2020)","journal-title":"Med. Image Anal."},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Zou, Y., et al.: Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 289\u2013305 (2018)","DOI":"10.1007\/978-3-030-01219-9_18"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09037-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T15:28:56Z","timestamp":1687879736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09037-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031090363","9783031090370"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09037-0_20","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":"2 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPRAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Paris","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icprai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icprai2022.sciencesconf.org\/1.6.If","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}