{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:48:42Z","timestamp":1767340122314,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031777851"},{"type":"electronic","value":"9783031777868"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-77786-8_1","type":"book-chapter","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T11:08:38Z","timestamp":1737025718000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deployable Microscopic Image Segmentation Look-Up Table Based on A Dilated CNN"],"prefix":"10.1007","author":[{"given":"Yunheng","family":"Wu","sequence":"first","affiliation":[]},{"given":"Jiazhen","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Shuntaro","family":"Kawamura","sequence":"additional","affiliation":[]},{"given":"Masahiro","family":"Oda","sequence":"additional","affiliation":[]},{"given":"Yuichiro","family":"Hayashi","sequence":"additional","affiliation":[]},{"given":"Takanori","family":"Takebe","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"given":"Kensaku","family":"Mori","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102358","volume":"77","author":"M Molina-Moreno","year":"2022","unstructured":"Molina-Moreno, M., et al.: ACME: automatic feature extraction for cell migration examination through intravital microscopy imaging. Med. Image Anal. 77, 102358 (2022)","journal-title":"Med. Image Anal."},{"issue":"7893","key":"1_CR2","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/s41586-021-04263-y","volume":"601","author":"G Crainiciuc","year":"2022","unstructured":"Crainiciuc, G., et al.: Behavioural immune landscapes of inflammation. Nature 601(7893), 415\u2013421 (2022)","journal-title":"Nature"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.micron.2014.04.001","volume":"65","author":"M Saraswat","year":"2014","unstructured":"Saraswat, M., Arya, K.: Automated microscopic image analysis for leukocytes identification: a survey. Micron 65, 20\u201333 (2014)","journal-title":"Micron"},{"issue":"11","key":"1_CR4","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1038\/s41568-023-00610-5","volume":"23","author":"M Alieva","year":"2023","unstructured":"Alieva, M., Wezenaar, A.K., Wehrens, E.J., Rios, A.C.: Bridging live-cell imaging and next-generation cancer treatment. Nat. Rev. Cancer 23(11), 731\u2013745 (2023)","journal-title":"Nat. Rev. Cancer"},{"issue":"1","key":"1_CR5","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."},{"issue":"7","key":"1_CR6","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1177\/25.7.70454","volume":"25","author":"GW Zack","year":"1977","unstructured":"Zack, G.W., Rogers, W.E., Latt, S.A.: Automatic measurement of sister chromatid exchange frequency. J. Histochem. Cytochem. 25(7), 741\u2013753 (1977)","journal-title":"J. Histochem. Cytochem."},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Tran, T., Kwon, O.H., Kwon, K.R., Lee, S.H., Kang, K.W.: Blood cell images segmentation using deep learning semantic segmentation. In: 2018 IEEE International Conference on Electronics and Communication Engineering (ICECE), pp. 13\u201316. IEEE (2018)","DOI":"10.1109\/ICECOME.2018.8644754"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.media.2018.12.003","volume":"52","author":"SEA Raza","year":"2019","unstructured":"Raza, S.E.A., et al.: Micro-Net: a unified model for segmentation of various objects in microscopy images. Med. Image Anal. 52, 160\u2013173 (2019)","journal-title":"Med. Image Anal."},{"issue":"12","key":"1_CR10","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1038\/s41592-019-0403-1","volume":"16","author":"E Moen","year":"2019","unstructured":"Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., Van Valen, D.: Deep learning for cellular image analysis. Nat. Methods 16(12), 1233\u20131246 (2019)","journal-title":"Nat. Methods"},{"issue":"4","key":"1_CR11","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s42256-021-00305-2","volume":"3","author":"E Korot","year":"2021","unstructured":"Korot, E., et al.: Code-free deep learning for multi-modality medical image classification. Nat. Mach. Intell. 3(4), 288\u2013298 (2021)","journal-title":"Nat. Mach. Intell."},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"issue":"7","key":"1_CR14","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nmeth.2019","volume":"9","author":"J Schindelin","year":"2012","unstructured":"Schindelin, J., et al.: Fiji: an open-source platform for biological-image analysis. Nat. Methods 9(7), 676\u2013682 (2012)","journal-title":"Nat. Methods"},{"key":"1_CR15","unstructured":"Carlos, G., et al.: SAMJ-IJ: an ImageJ\/Fiji plugin designed to effortlessly integrate Segment-Anything models (SAMs). https:\/\/github.com\/segment-anything-models-java\/SAMJ-IJ. Accessed 09 Jun 2024"},{"issue":"5","key":"1_CR16","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1109\/TMI.2021.3056678","volume":"40","author":"B Yu","year":"2021","unstructured":"Yu, B., et al.: SA-LuT-Nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation. IEEE Trans. Med. Imaging 40(5), 1417\u20131427 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"1_CR17","doi-asserted-by":"publisher","first-page":"1764","DOI":"10.1038\/s41467-024-45827-6","volume":"15","author":"TF Mertens","year":"2024","unstructured":"Mertens, T.F., et al.: MarShie: a clearing protocol for 3D analysis of single cells throughout the bone marrow at subcellular resolution. Nat. Commun. 15(1), 1764 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"1_CR18","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1038\/s41467-024-45788-w","volume":"15","author":"G Sicoli","year":"2024","unstructured":"Sicoli, G., et al.: Large dynamics of a phase separating arginine-glycine-rich domain revealed via nuclear and electron spins. Nat. Commun. 15(1), 1610 (2024)","journal-title":"Nat. Commun."},{"issue":"1","key":"1_CR19","doi-asserted-by":"publisher","first-page":"4284","DOI":"10.1038\/s41467-020-17700-9","volume":"11","author":"I Antoniadi","year":"2020","unstructured":"Antoniadi, I., et al.: Cell-surface receptors enable perception of extracellular cytokinins. Nat. Commun. 11(1), 4284 (2020)","journal-title":"Nat. Commun."},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Jo, Y., Kim, S.J.: Practical single-image super-resolution using look-up table. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 691\u2013700 (2021)","DOI":"10.1109\/CVPR46437.2021.00075"},{"key":"1_CR21","unstructured":"Kim, Y.T., Cho, Y.H., Lee, C.H., Ha, Y.H.: Color look-up table design for gamut mapping and color space conversion. In: International Conference on Digital Production Printing and Industrial Applications, pp. 28\u201329 (2003)"},{"key":"1_CR22","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"1_CR23","unstructured":"Paszke, A., et\u00a0al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"issue":"12","key":"1_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0027281","volume":"6","author":"E B\u00e1rtov\u00e1","year":"2011","unstructured":"B\u00e1rtov\u00e1, E., \u0160ust\u00e1\u010dkov\u00e1, G., Stixov\u00e1, L., Kozubek, S., Legartov\u00e1, S., Folt\u00e1nkov\u00e1, V.: Recruitment of oct4 protein to UV-damaged chromatin in embryonic stem cells. PLoS ONE 6(12), e27281 (2011)","journal-title":"PLoS ONE"},{"issue":"12","key":"1_CR25","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"},{"key":"1_CR26","unstructured":"Ma\u0161ka, M., et\u00a0al.: The cell tracking challenge: 10 years of objective benchmarking. Nat. Methods 1\u201311 (2023)"},{"key":"1_CR27","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Chowdary, G.J., Yin, Z.: Diffusion transformer U-Net for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 622\u2013631 (2023)","DOI":"10.1007\/978-3-031-43901-8_59"},{"key":"1_CR30","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022)","DOI":"10.1007\/978-3-031-25066-8_9"}],"container-title":["Lecture Notes in Computer Science","Medical Optical Imaging and Virtual Microscopy Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77786-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T11:08:49Z","timestamp":1737025729000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77786-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031777851","9783031777868"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77786-8_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Authors have no competing interests in the paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MOVI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"movi2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/movi2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}