{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:27:09Z","timestamp":1781368029255,"version":"3.54.1"},"reference-count":172,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T00:00:00Z","timestamp":1702166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T00:00:00Z","timestamp":1702166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806047"],"award-info":[{"award-number":["61806047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s00521-023-09284-4","type":"journal-article","created":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T11:01:26Z","timestamp":1702206086000},"page":"3317-3346","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification"],"prefix":"10.1007","volume":"36","author":[{"given":"Jian","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1545-8885","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Islam Mohammad","family":"Shariful","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xintong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,10]]},"reference":[{"key":"9284_CR1","volume-title":"Microscope image processing","author":"Q Wu","year":"2010","unstructured":"Wu Q, Merchant F, Castleman K (2010) Microscope image processing. Elsevier, Amsterdam"},{"issue":"13","key":"9284_CR2","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1063\/1.112931","volume":"65","author":"F Zenhausern","year":"1994","unstructured":"Zenhausern F, Boyle M, Wickramasinghe H (1994) Apertureless near-field optical microscope. Appl Phys Lett 65(13):1623\u20131625","journal-title":"Appl Phys Lett"},{"issue":"24","key":"9284_CR3","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1063\/1.106801","volume":"60","author":"R Toledo-Crow","year":"1992","unstructured":"Toledo-Crow R, Yang P, Chen Y, Vaez-Iravani M (1992) Near-field differential scanning optical microscope with atomic force regulation. Appl Phys Lett 60(24):2957\u20132959","journal-title":"Appl Phys Lett"},{"issue":"3","key":"9284_CR4","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1364\/OL.19.000159","volume":"19","author":"Y Inouye","year":"1994","unstructured":"Inouye Y, Kawata S (1994) Near-field scanning optical microscope with a metallic probe tip. Opt Lett 19(3):159\u2013161","journal-title":"Opt Lett"},{"key":"9284_CR5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-1-4757-2519-3_1","volume-title":"The transmission electron microscope","author":"D Williams","year":"1996","unstructured":"Williams D, Carter C (1996) The transmission electron microscope. Springer, Berlin, pp 3\u201317"},{"issue":"11","key":"9284_CR6","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1063\/1.332840","volume":"54","author":"H Seiler","year":"1983","unstructured":"Seiler H (1983) Secondary electron emission in the scanning electron microscope. J Appl Phys 54(11):R1\u2013R18","journal-title":"J Appl Phys"},{"issue":"2","key":"9284_CR7","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1103\/PhysRevB.31.805","volume":"31","author":"J Tersoff","year":"1985","unstructured":"Tersoff J, Hamann D (1985) Theory of the scanning tunneling microscope. Phys Rev B 31(2):805","journal-title":"Phys Rev B"},{"issue":"9","key":"9284_CR8","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1103\/PhysRevLett.56.930","volume":"56","author":"G Binnig","year":"1986","unstructured":"Binnig G, Quate C, Gerber C (1986) Atomic force microscope. Phys Rev Lett 56(9):930","journal-title":"Phys Rev Lett"},{"issue":"8","key":"9284_CR9","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1364\/OL.7.000350","volume":"7","author":"M Duncan","year":"1982","unstructured":"Duncan M, Reintjes J, Manuccia T (1982) Scanning coherent anti-Stokes Raman microscope. Opt Lett 7(8):350\u2013352","journal-title":"Opt Lett"},{"issue":"5954","key":"9284_CR10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/308032a0","volume":"308","author":"M Adrian","year":"1984","unstructured":"Adrian M, Dubochet J, Lepault J, McDowall A (1984) Cryo-electron microscopy of viruses. Nature 308(5954):32\u201336","journal-title":"Nature"},{"key":"9284_CR11","doi-asserted-by":"crossref","unstructured":"Li C, Zhang J, Kulwa F, Qi S, Qi Z (2020) A SARS-CoV-2 microscopic image dataset with ground truth images and visual features. In Proc. of PRCV 2020, pp 244\u2013255","DOI":"10.1007\/978-3-030-60633-6_20"},{"issue":"4","key":"9284_CR12","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s10462-017-9572-4","volume":"51","author":"C Li","year":"2019","unstructured":"Li C, Wang K, Xu N (2019) A survey for the applications of content-based microscopic image analysis in microorganism classification domains. Artif Intell Rev 51(4):577\u2013646","journal-title":"Artif Intell Rev"},{"key":"9284_CR13","doi-asserted-by":"crossref","first-page":"107885","DOI":"10.1016\/j.patcog.2021.107885","volume":"115","author":"J Zhang","year":"2021","unstructured":"Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Z, Li H (2021) LCU-Net: a novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recogn 115:107885","journal-title":"Pattern Recogn"},{"issue":"6","key":"9284_CR14","doi-asserted-by":"crossref","first-page":"4809","DOI":"10.1007\/s10462-021-10121-0","volume":"55","author":"X Li","year":"2022","unstructured":"Li X, Li C, Rahaman M, Li X, Sun H, Zhang H, Zhang Y, Li X, Wu J, Yao Y (2022) A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification, and detection approaches. Art Intell Rev 55(6):4809\u20134878","journal-title":"Art Intell Rev"},{"key":"9284_CR15","doi-asserted-by":"crossref","first-page":"90931","DOI":"10.1109\/ACCESS.2020.2993788","volume":"8","author":"X Zhouand","year":"2020","unstructured":"Zhouand X, Li C, Rahaman M, Yao Y, Ai S, Sun C, Wang Q, Zhang Y, Li M, Li X (2020) A comprehensive review for breast histopathology image analysis using classical and deep neural networks. IEEE Access 8:90931\u201390956","journal-title":"IEEE Access"},{"key":"9284_CR16","doi-asserted-by":"crossref","first-page":"61687","DOI":"10.1109\/ACCESS.2020.2983186","volume":"8","author":"M Rahaman","year":"2020","unstructured":"Rahaman M, Li C, Wu X, Yao Y, Hu Z, Jiang T, Li X, Qi S (2020) A survey for cervical cytopathology image analysis using deep learning. IEEE Access 8:61687\u201361710","journal-title":"IEEE Access"},{"issue":"2","key":"9284_CR17","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1166\/jamr.2015.1245","volume":"10","author":"S Hore","year":"2015","unstructured":"Hore S, Chakroborty S, Ashour A, Dey N, Ashour A, Sifaki-Pistolla D, Bhattacharya T, Chaudhuri S (2015) Finding contours of hippocampus brain cell using microscopic image analysis. J Adv Microsc Res 10(2):93\u2013103","journal-title":"J Adv Microsc Res"},{"issue":"11","key":"9284_CR18","doi-asserted-by":"crossref","first-page":"5370","DOI":"10.1021\/cg501386j","volume":"14","author":"S \u00d8ien","year":"2014","unstructured":"\u00d8ien S, Wragg D, Reinsch H, Svelle S, Bordiga S, Lamberti C, Lillerud K (2014) Detailed structure analysis of atomic positions and defects in zirconium metal-organic frameworks. Crystal Growth Des 14(11):5370\u20135372","journal-title":"Crystal Growth Des"},{"issue":"04","key":"9284_CR19","doi-asserted-by":"crossref","first-page":"437","DOI":"10.2118\/20920-PA","volume":"6","author":"W Clelland","year":"1991","unstructured":"Clelland W, Fens T (1991) Automated rock characterization with SEM\/image-analysis techniques. SPE Form Eval 6(04):437\u2013443","journal-title":"SPE Form Eval"},{"key":"9284_CR20","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/978-1-4615-0085-8_2","volume-title":"Physical methods in agriculture","author":"M Pagliai","year":"2002","unstructured":"Pagliai M, Vignozzi N (2002) Image analysis and microscopic techniques to characterize soil pore system. In: Blahovec J, Kutilek M (eds) Physical methods in agriculture. Springer, Berlin, pp 13\u201338"},{"issue":"1","key":"9284_CR21","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1006\/jcis.1998.5986","volume":"211","author":"A Abell","year":"1999","unstructured":"Abell A, Willis K, Lange D (1999) Mercury intrusion porosimetry and image analysis of cement-based materials. J Colloid Interface Sci 211(1):39\u201344","journal-title":"J Colloid Interface Sci"},{"issue":"1","key":"9284_CR22","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1146\/annurev.py.33.090195.002421","volume":"33","author":"H Nilsson","year":"1995","unstructured":"Nilsson H (1995) Remote sensing and image analysis in plant pathology. Annu Rev Phytopathol 33(1):489\u2013528","journal-title":"Annu Rev Phytopathol"},{"key":"9284_CR23","volume-title":"Principles of artificial intelligence","author":"N Nilsson","year":"2014","unstructured":"Nilsson N (2014) Principles of artificial intelligence. Morgan Kaufmann, Burlington"},{"key":"9284_CR24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi B, Setio A, Ciompi F, Ghafoorian M, Laak J, Ginneken B, S\u00e1nchez C (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"9284_CR25","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In Proc. of ICMICCA 2015, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9284_CR26","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proc. of CVPR 2015, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"1","key":"9284_CR27","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s11831-022-09811-x","volume":"30","author":"J Zhang","year":"2023","unstructured":"Zhang J, Li C, Rahaman MM, Yao Y, Ma P, Zhang J, Zhao X, Jiang T, Grzegorzek M (2023) A comprehensive survey with quantitative comparison of image analysis methods for microorganism biovolume measurements. Arch Comput Methods Eng 30(1):639\u2013673","journal-title":"Arch Comput Methods Eng"},{"key":"9284_CR28","doi-asserted-by":"crossref","first-page":"107034","DOI":"10.1016\/j.compbiomed.2023.107034","volume":"161","author":"Hu Weiming","year":"2023","unstructured":"Weiming Hu, Li Xintong, Li Chen, Li Rui, Jiang Tao, Sun Hongzan, Huang Xinyu, Grzegorzek Marcin, Li Xiaoyan (2023) A state-of-the-art survey of artificial neural networks for whole-slide image analysis: from popular convolutional neural networks to potential visual transformers. Comput Biol Med 161:107034","journal-title":"Comput Biol Med"},{"issue":"2","key":"9284_CR29","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1007\/s10462-022-10209-1","volume":"56","author":"Pingli Ma","year":"2023","unstructured":"Ma Pingli, Li Chen, Rahaman Md Mamunur, Yao Yudong, Zhang Jiawei, Zou Shuojia, Zhao Xin, Grzegorzek Marcin (2023) A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Artif Intell Rev 56(2):1627\u20131698","journal-title":"Artif Intell Rev"},{"issue":"4","key":"9284_CR30","doi-asserted-by":"crossref","first-page":"2875","DOI":"10.1007\/s10462-021-10082-4","volume":"55","author":"J Zhang","year":"2022","unstructured":"Zhang J, Li C, Rahaman MM, Yao Y, Ma P, Zhang J, Zhao X, Jiang T, Grzegorzek M (2022) A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Art Intell Rev 55(4):2875\u20132944","journal-title":"Art Intell Rev"},{"key":"9284_CR31","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S Taghanaki","year":"2020","unstructured":"Taghanaki S, Abhishek K, Cohen J, Cohen-Adad J, Hamarneh G (2020) Deep semantic segmentation of natural and medical images: a review. Art Intell Rev 54:137\u2013178","journal-title":"Art Intell Rev"},{"issue":"2","key":"9284_CR32","first-page":"20508","volume":"64","author":"G Du","year":"2020","unstructured":"Du G, Cao X, Liang J, Chen X, Zhan Y (2020) Medical image segmentation based on u-net: a review. J Imaging Sci Technol 64(2):20508","journal-title":"J Imaging Sci Technol"},{"key":"9284_CR33","doi-asserted-by":"crossref","unstructured":"Colonna A, Scarpa F, Ruggeri A (2018) Segmentation of corneal nerves using a u-net-based convolutional neural network. In: Computational pathology and ophthalmic medical image analysis, pp 185\u2013192","DOI":"10.1007\/978-3-030-00949-6_22"},{"key":"9284_CR34","doi-asserted-by":"crossref","unstructured":"Seong S, Park H (2019) Automated identification of neural cells in the multi-photon images using deep-neural networks. arXiv: 1909.11269","DOI":"10.1016\/j.ibror.2019.07.439"},{"issue":"1","key":"9284_CR35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"M Daniel","year":"2019","unstructured":"Daniel M, Atzrodt L, Bucher F, Wacker K, B\u00f6hringer S, Reinhard T, B\u00f6hringer D (2019) Automated segmentation of the corneal endothelium in a large set of \u2018real-world\u2019specular microscopy images using the U-Net architecture. Sci Rep 9(1):1\u20137","journal-title":"Sci Rep"},{"key":"9284_CR36","unstructured":"N\u00fa\u00f1ez-Fern\u00e1ndez D, Ballan L, Jim\u00e9nez-Avalos G, Coronel J, Zimic M(2020) Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images. arXiv: 2007.02482"},{"key":"9284_CR37","doi-asserted-by":"crossref","unstructured":"Ojeda-Pat A, Martin-Gonzalez A, Soberanis-Mukul R (2020) Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation. In: Proc. of ISICS 2020, pp 118\u2013131","DOI":"10.1007\/978-3-030-43364-2_11"},{"key":"9284_CR38","doi-asserted-by":"crossref","first-page":"104450","DOI":"10.1016\/j.cageo.2020.104450","volume":"138","author":"Z Chen","year":"2020","unstructured":"Chen Z, Liu X, Yang J, Little E, Zhou Y (2020) Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin. Comput. Geosci. 138:104450","journal-title":"Comput. Geosci."},{"key":"9284_CR39","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.micron.2019.02.009","volume":"120","author":"A Oktay","year":"2019","unstructured":"Oktay A, Gurses A (2019) Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images. Micron 120:113\u2013119","journal-title":"Micron"},{"issue":"1","key":"9284_CR40","doi-asserted-by":"crossref","first-page":"015015","DOI":"10.1088\/2632-2153\/abc81c","volume":"2","author":"S Farley","year":"2020","unstructured":"Farley S, Hodgkinson J, Gordon O, Turner J, Soltoggio A, Moriarty P, Hunsicker E (2020) Improving the segmentation of scanning probe microscope images using convolutional neural networks. Mach Learn Sci Technol 2(1):015015","journal-title":"Mach Learn Sci Technol"},{"key":"9284_CR41","doi-asserted-by":"crossref","first-page":"5049390","DOI":"10.1155\/2018\/5049390","volume":"2018","author":"J Jaworek-Korjakowska","year":"2018","unstructured":"Jaworek-Korjakowska J (2018) A deep learning approach to vascular structure segmentation in dermoscopy colour images. BioMed Res. Int. 2018:5049390\u20135049390","journal-title":"BioMed Res. Int."},{"key":"9284_CR42","doi-asserted-by":"crossref","unstructured":"Meyer M, Costa P, Galdran A, Mendon\u00e7a A, Campilho A (2017) A deep neural network for vessel segmentation of scanning laser ophthalmoscopy images. In: Proc. of ICIAR 2017, pp 507\u2013515","DOI":"10.1007\/978-3-319-59876-5_56"},{"issue":"17","key":"9284_CR43","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.1364\/AO.19.002991","volume":"19","author":"R Webb","year":"1980","unstructured":"Webb R, Hughes G, Pomerantzeff O (1980) Flying spot TV ophthalmoscope. Appl Opt 19(17):2991\u20132997","journal-title":"Appl Opt"},{"issue":"12","key":"9284_CR44","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/TMI.2016.2587062","volume":"35","author":"J Zhang","year":"2016","unstructured":"Zhang J, Dashtbozorg B, Bekkers E, Pluim J, Duits R, Romeny B (2016) Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans Med Imaging 35(12):2631\u20132644","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"9284_CR45","first-page":"849","volume":"66","author":"Z Swiderska-Chadaj","year":"2018","unstructured":"Swiderska-Chadaj Z, Markiewicz T, Gallego J, Bueno G, Grala B, Lorent M (2018) Deep learning for damaged tissue detection and segmentation in Ki-7 brain tumor specimens based on the U-net model. Bulletin of the polish academy of sciences. Tech Sci 66(6):849\u2013856","journal-title":"Tech Sci"},{"key":"9284_CR46","doi-asserted-by":"crossref","unstructured":"learning dense volumetric segmentation from sparse annotation (2016) \u00d6zg\u00fcn \u00c7i\u00e7ek, A. Abdulkadir, S. Lienkamp, T. Brox, and O. Ronneberger. 3D U-Net. In: Proceeding of ICMICCAI 2016, pp 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"issue":"6","key":"9284_CR47","doi-asserted-by":"crossref","first-page":"061005","DOI":"10.3788\/LOP56.061005","volume":"56","author":"Z Fang","year":"2019","unstructured":"Fang Z, Yue W, Zhitao X, Lei G, Jun W, Yanbei L, Wen W (2019) Nanoparticle segmentation based on U-Net convolutional neural network. Laser Optoelectron. Progr 56(6):061005","journal-title":"Laser Optoelectron. Progr"},{"key":"9284_CR48","doi-asserted-by":"crossref","unstructured":"Fu C, Lee S, Ho D, Han S, Salama P, Dunn K, Delp E (2018) Three dimensional fluorescence microscopy image synthesis and segmentation. In: Proceeding of CVPR 2018, pp 2221\u20132229","DOI":"10.1109\/CVPRW.2018.00298"},{"key":"9284_CR49","doi-asserted-by":"crossref","unstructured":"Eschweiler D, Spina T, Choudhury R, Meyerowitz E, Cunha A, Stegmaier J (2019) CNN-based preprocessing to optimize watershed-based cell segmentation in 3D confocal microscopy images. In: Proceeding of ISBI 2019, pp 223\u2013227","DOI":"10.1109\/ISBI.2019.8759242"},{"issue":"51","key":"9284_CR50","doi-asserted-by":"crossref","first-page":"E8238","DOI":"10.1073\/pnas.1616768113","volume":"113","author":"L Willis","year":"2016","unstructured":"Willis L, Refahi Y, Wightman R, Landrein B, Jos\u00e9 Teles, Huang K, Meyerowitz E, Henrik J\u00f6nsson (2016) Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche. Proc Natl Acad Sci 113(51):E8238\u2013E8246","journal-title":"Proc Natl Acad Sci"},{"issue":"7","key":"9284_CR51","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/nmeth.1472","volume":"7","author":"R Fernandez","year":"2010","unstructured":"Fernandez R, Das P, Mirabet V, Moscardi E, Traas J, Verdeil J, Malandain G, Godin C (2010) Imaging plant growth in 4D: robust tissue reconstruction and lineaging at cell resolution. Nat Methods 7(7):547","journal-title":"Nat Methods"},{"issue":"12","key":"9284_CR52","doi-asserted-by":"crossref","first-page":"e1002780","DOI":"10.1371\/journal.pcbi.1002780","volume":"8","author":"K Mosaliganti","year":"2012","unstructured":"Mosaliganti K, Noche R, Xiong F, Swinburne I, Megason S (2012) ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes. PLoS Comput Biol 8(12):e1002780","journal-title":"PLoS Comput Biol"},{"key":"9284_CR53","doi-asserted-by":"crossref","unstructured":"Heinrich L, Funke J, Pape C, Nunez-Iglesias J, Saalfeld S (2018) Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain. In: Proceeding of ICMICCAI 2018, pp 317\u2013325","DOI":"10.1007\/978-3-030-00934-2_36"},{"key":"9284_CR54","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang D, Song Y, Liu S, Wang Y, Feng D, Peng H, Cai W (2019) Segmenting neuronal structure in 3D optical microscope images via knowledge distillation with teacher-student network. In: Proceeding of ISBI 2019, pp 228\u2013231","DOI":"10.1109\/ISBI.2019.8759326"},{"key":"9284_CR55","unstructured":"Zhang M, Li X, Xu M, Li Q (2017) Image segmentation and classification for sickle cell disease using deformable u-net. arXiv: 1710.08149"},{"key":"9284_CR56","doi-asserted-by":"crossref","unstructured":"Zhang M, Li X, Xu X, Li Q (2018) RBC semantic segmentation for sickle cell disease based on deformable U-Net. In: Proceeding of ICMICCAI 2018, pp 695\u2013702","DOI":"10.1007\/978-3-030-00937-3_79"},{"issue":"10","key":"9284_CR57","doi-asserted-by":"crossref","first-page":"e1005746","DOI":"10.1371\/journal.pcbi.1005746","volume":"13","author":"X Xu","year":"2017","unstructured":"Xu X, Papageorgiou D, Abidi S, Dao M, Zhao H, Karniadakis G (2017) A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput Biol 13(10):e1005746","journal-title":"PLoS Comput Biol"},{"issue":"8","key":"9284_CR58","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.3390\/sym12081230","volume":"12","author":"X Qin","year":"2020","unstructured":"Qin X, Wu C, Chang H, Lu H, Zhang X (2020) Match feature U-Net: dynamic receptive field networks for biomedical image segmentation. Symmetry 12(8):1230","journal-title":"Symmetry"},{"key":"9284_CR59","doi-asserted-by":"crossref","unstructured":"Rad R, Saeedi P, Au J, Havelock J (2018) Blastomere cell counting and centroid localization in microscopic images of human embryo. In: Proceeding of MMSP 2018, pp 1\u20136","DOI":"10.1109\/MMSP.2018.8547107"},{"key":"9284_CR60","doi-asserted-by":"crossref","first-page":"101612","DOI":"10.1016\/j.media.2019.101612","volume":"62","author":"R Rad","year":"2020","unstructured":"Rad R, Saeedi P, Au J, Havelock J (2020) Trophectoderm segmentation in human embryo images via inceptioned U-Net. Med Image Anal 62:101612","journal-title":"Med Image Anal"},{"key":"9284_CR61","first-page":"1","volume":"2015","author":"C Szegedy","year":"2015","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In Proceeding of CVPR 2015:1\u20139","journal-title":"In Proceeding of CVPR"},{"issue":"12","key":"9284_CR62","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1109\/TBME.2017.2759665","volume":"64","author":"P Saeedi","year":"2017","unstructured":"Saeedi P, Yee D, Au J, Havelock J (2017) Automatic identification of human blastocyst components via texture. IEEE Trans Biomed Eng 64(12):2968\u20132978","journal-title":"IEEE Trans Biomed Eng"},{"key":"9284_CR63","doi-asserted-by":"crossref","unstructured":"Matuszewski D, Sintorn I (2018) Minimal annotation training for segmentation of microscopy images. In: Proceeding of ISBI 2018, pp 387\u2013390","DOI":"10.1109\/ISBI.2018.8363599"},{"issue":"2","key":"9284_CR64","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1111\/j.1365-2818.2011.03556.x","volume":"245","author":"G Kylberg","year":"2012","unstructured":"Kylberg G, Uppstr\u00f6m M, HEDLUND K, Borgefors G, SINTORN I (2012) Segmentation of virus particle candidates in transmission electron microscopy images. J Microsc 245(2):140\u2013147","journal-title":"J Microsc"},{"key":"9284_CR65","doi-asserted-by":"crossref","unstructured":"Mocan I, Itu R, Ciurte A, Danescu R, Buiga R (2018) Automatic Detection of Tumor Cells in Microscopic Images of Unstained Blood using Convolutional Neural Networks. In: Proceeding of ICCP 2018, pp 319\u2013324","DOI":"10.1109\/ICCP.2018.8516638"},{"key":"9284_CR66","doi-asserted-by":"crossref","unstructured":"Xu Z, Sobhani F, Moro C, Zhang Q (2019) Us-net for robust and efficient nuclei instance segmentation. In: Proceeding of ISBI 2019, pp 44\u201347","DOI":"10.1109\/ISBI.2019.8759530"},{"key":"9284_CR67","doi-asserted-by":"crossref","unstructured":"Li W, Qian X, Ji J (2017) Noise-tolerant deep learning for histopathological image segmentation. In: Proc. of ICIP 2017, pp 3075\u20133079","DOI":"10.1109\/ICIP.2017.8296848"},{"key":"9284_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2018.04.004","volume":"88","author":"A Fabija\u0144ska","year":"2018","unstructured":"Fabija\u0144ska A (2018) Segmentation of corneal endothelium images using a U-Net-based convolutional neural network. Artif Intell Med 88:1\u201313","journal-title":"Artif Intell Med"},{"key":"9284_CR69","doi-asserted-by":"crossref","unstructured":"Kumar C, TN M, Narasimhadhan A (2020) Cell Segmentation by Modified U-Net Architecture for Biomedical Images. In: Proceeding of CONECCT 2020, pp 1\u20136","DOI":"10.1109\/CONECCT50063.2020.9198530"},{"key":"9284_CR70","doi-asserted-by":"crossref","unstructured":"Berm\u00fadez-Chac\u00f3n R, M\u00e1rquez-Neila P, Salzmann M, Fua P (2018) A domain-adaptive two-stream U-Net for electron microscopy image segmentation. In: Proceeding of ISBI 2018, pp 400\u2013404","DOI":"10.1109\/ISBI.2018.8363602"},{"key":"9284_CR71","doi-asserted-by":"crossref","unstructured":"Jha D, Riegler M, Johansen D, Halvorsen P, Johansen H (2020) Doubleu-net: A deep convolutional neural network for medical image segmentation. In: Proceeding of CBMS 2020, pp 558\u2013564","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"9284_CR72","unstructured":"Zhuang J (2018) Laddernet: Multi-path networks based on u-net for medical image segmentation. arXiv: 1810.07810"},{"key":"9284_CR73","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556"},{"key":"9284_CR74","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez F, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111","journal-title":"Comput Med Imaging Graph"},{"key":"9284_CR75","doi-asserted-by":"crossref","unstructured":"Torr A, Basaran D, Sero J, Rittscher J, Sailem H (2020) DeepSplit: Segmentation of Microscopy Images Using Multi-Task Convolutional Networks. In: Annual conference on medical image understanding and analysis, pp 155\u2013167","DOI":"10.1007\/978-3-030-52791-4_13"},{"key":"9284_CR76","doi-asserted-by":"crossref","unstructured":"Bozkurt A, Kose K, Alessi-Fox C, Gill M, Dy J, Brooks D, Rajadhyaksha M (2018) A multiresolution convolutional neural network with partial label training for annotating reflectance confocal microscopy images of skin. In: Proceeding of ICMICCAI 2018, pp 292\u2013299","DOI":"10.1007\/978-3-030-00934-2_33"},{"key":"9284_CR77","doi-asserted-by":"crossref","first-page":"101786","DOI":"10.1016\/j.media.2020.101786","volume":"65","author":"B Zhao","year":"2020","unstructured":"Zhao B, Chen X, Li Z, Yu Z, Yao S, Yan L, Wang Y, Liu Z, Liang C, Han C (2020) Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation. Med Image Anal 65:101786","journal-title":"Med Image Anal"},{"key":"9284_CR78","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L, Weinberger K (2017) Densely connected convolutional networks. In: Proceeding of CVPR 2017, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"9284_CR79","doi-asserted-by":"crossref","unstructured":"Vahadane Abhishek, Atheeth B, Majumdar Shantanu (2021) Dual encoder attention u-net for nuclei segmentation. In: 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC), IEEE, pp 3205\u20133208","DOI":"10.1109\/EMBC46164.2021.9630037"},{"key":"9284_CR80","doi-asserted-by":"crossref","first-page":"53","DOI":"10.3389\/fbioe.2019.00053","volume":"7","author":"Vu Quoc Dang","year":"2019","unstructured":"Dang Vu Quoc, Simon Graham, Tahsin Kurc, Nhat To Minh Nguyen, Muhammad Shaban, Talha Qaiser, Alemi Koohbanani Navid, Ali Khurram Syed, Jayashree Kalpathy-Cramer, Tianhao Zhao et al (2019) Methods for segmentation and classification of digital microscopy tissue images. Front Bioeng Biotechnol 7:53","journal-title":"Front Bioeng Biotechnol"},{"key":"9284_CR81","unstructured":"Oktay O, Schlemper J, Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N, Kainz B (2018) Attention u-net: Learning where to look for the pancreas. arXiv: 1804.03999"},{"key":"9284_CR82","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.jvcir.2018.10.001","volume":"56","author":"S Lian","year":"2018","unstructured":"Lian S, Luo Z, Zhong Z, Lin X, Su S, Li S (2018) Attention guided U-Net for accurate iris segmentation. J Vis Commun Image Represent 56:296\u2013304","journal-title":"J Vis Commun Image Represent"},{"issue":"8","key":"9284_CR83","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TPAMI.2009.66","volume":"32","author":"H Proen\u00e7a","year":"2009","unstructured":"Proen\u00e7a H, Filipe S, Santos R, Oliveira J, Alexandre L (2009) The UBIRIS. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell 32(8):1529\u20131535","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9284_CR84","doi-asserted-by":"crossref","first-page":"32826","DOI":"10.1109\/ACCESS.2020.2974027","volume":"8","author":"Y Lv","year":"2020","unstructured":"Lv Y, Ma H, Li J, Liu S (2020) Attention guided U-Net with atrous convolution for accurate retinal vessels segmentation. IEEE Access 8:32826\u201332839","journal-title":"IEEE Access"},{"key":"9284_CR85","unstructured":"Bansal N, Dutta M (2013) Retina vessels detection algorithm for biomedical symptoms diagnosis. Int J Comput Appl, 71(20)"},{"key":"9284_CR86","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.measurement.2018.05.003","volume":"125","author":"Y Guo","year":"2018","unstructured":"Guo Y, Budak \u00dc, Vespa L, Khorasani E, \u015eeng\u00fcr A (2018) A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy. Measurement 125:586\u2013591","journal-title":"Measurement"},{"issue":"5","key":"9284_CR87","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1049\/iet-ipr.2017.0284","volume":"12","author":"S Thangaraj","year":"2018","unstructured":"Thangaraj S, Periyasamy V, Balaji R (2018) Retinal vessel segmentation using neural network. IET Image Proc 12(5):669\u2013678","journal-title":"IET Image Proc"},{"key":"9284_CR88","doi-asserted-by":"crossref","unstructured":"Mou L, Zhao Y, Chen L, Cheng J, Gu Z, Hao H, Qi H, Zheng Y, Frangi A, Liu J (2019) CS-Net: channel and spatial attention network for curvilinear structure segmentation. In: Proceeding of ICMICCAI 2019, pp 721\u2013730","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"9284_CR89","unstructured":"Li R, Li M, Li J, Zhou Y (2019) Connection sensitive attention u-net for accurate retinal vessel segmentation. arXiv:1903.05558"},{"issue":"11","key":"9284_CR90","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.3390\/app10113777","volume":"10","author":"Y Jiang","year":"2020","unstructured":"Jiang Y, Wang F, Gao J, Cao S (2020) Multi-path recurrent u-net segmentation of retinal fundus image. Appl Sci 10(11):3777","journal-title":"Appl Sci"},{"key":"9284_CR91","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2019.12.036","volume":"383","author":"H Zhang","year":"2020","unstructured":"Zhang H, Zhu H, Ling X (2020) Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk. Neurocomputing 383:212\u2013223","journal-title":"Neurocomputing"},{"key":"9284_CR92","unstructured":"Zhu N, Liu C, Singer Z, Danino T, Laine A, Guo J (2020) Segmentation with residual attention u-net and an edge-enhancement approach preserves cell shape features. arXiv:2001.05548"},{"issue":"1","key":"9284_CR93","first-page":"1004","volume":"2","author":"J Sivaswamy","year":"2015","unstructured":"Sivaswamy J, Krishnadas S, Chakravarty A, Joshi G, Tabish A (2015) A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomed Imaging Data Papers 2(1):1004","journal-title":"JSM Biomed Imaging Data Papers"},{"key":"9284_CR94","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceeding CVPR 2016, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9284_CR95","unstructured":"Xiancheng W, Wei L, Bingyi M, He J, Jiang Z, Xu W, Ji Z, Hong G, Zhaomeng S (2018) Retina blood vessel segmentation using a U-net based convolutional neural network. In: Procedia Computer Science, pp 1\u201311"},{"key":"9284_CR96","doi-asserted-by":"crossref","unstructured":"Leng J, Liu Y, Zhang T, Quan P, Cui Z (2018) Context-aware u-net for biomedical image segmentation. In: Proceeding of BIBM 2018, pp 2535\u20132538","DOI":"10.1109\/BIBM.2018.8621512"},{"key":"9284_CR97","doi-asserted-by":"crossref","unstructured":"Chidester B, Ton T, Tran M, Ma J, Do M (2019) Enhanced rotation-equivariant u-net for nuclear segmentation. In: Proceeding of CVPRW 2019","DOI":"10.1109\/CVPRW.2019.00143"},{"key":"9284_CR98","doi-asserted-by":"crossref","first-page":"107885","DOI":"10.1016\/j.patcog.2021.107885","volume":"115","author":"Jinghua Zhang","year":"2021","unstructured":"Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Zihan, Li H (2021) LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recogn 115:107885","journal-title":"Pattern Recogn"},{"key":"9284_CR99","doi-asserted-by":"crossref","unstructured":"Petit Olivi, Thome N, Rambour C, Themyr L, Collins T, Soler L (2021) U-net transformer: Self and cross attention for medical image segmentation. In: Machine Learning in medical imaging: 12th international workshop, MLMI 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings vol 12, Springer, pp 267\u2013276.","DOI":"10.1007\/978-3-030-87589-3_28"},{"key":"9284_CR100","unstructured":"Huang G, Chen D, Li T, Wu F, Maaten L, Weinberger K (2017) Multi-scale dense convolutional networks for efficient prediction. arXiv: 1703.09844, 2"},{"key":"9284_CR101","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceeding of CVPR 2017, pp 11\u201319","DOI":"10.1109\/CVPRW.2017.156"},{"key":"9284_CR102","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.3934\/mbe.2020175","volume":"17","author":"Y Cheng","year":"2020","unstructured":"Cheng Y, Ma M, Zhang L, Jin C, Ma L, Zhou Y (2020) Retinal blood vessel segmentation based on Densely Connected U-Net. Math Biosci Eng 17:3088\u20133108","journal-title":"Math Biosci Eng"},{"key":"9284_CR103","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceeding of ICCV 2015, pp 1026\u20131034","DOI":"10.1109\/ICCV.2015.123"},{"issue":"5","key":"9284_CR104","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1167\/iovs.08-3018","volume":"50","author":"C Owen","year":"2009","unstructured":"Owen C, Rudnicka A, Mullen R, Barman S, Monekosso D, Whincup P, Ng J, Paterson C (2009) Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) program. Investig Ophthalmol Visual Sci 50(5):2004\u20132010","journal-title":"Investig Ophthalmol Visual Sci"},{"issue":"2","key":"9284_CR105","doi-asserted-by":"crossref","first-page":"168","DOI":"10.3390\/e21020168","volume":"21","author":"C Wang","year":"2019","unstructured":"Wang C, Zhao Z, Ren Q, Xu Y, Yu Y (2019) Dense U-net based on patch-based learning for retinal vessel segmentation. Entropy 21(2):168","journal-title":"Entropy"},{"key":"9284_CR106","doi-asserted-by":"crossref","unstructured":"Samanta P, Raipuria G, Singhal N (2021) Context Aggregation Network For Semantic Labeling In Histopathology Images. In: Proceeding of ISBI 2021, pp 673\u2013676","DOI":"10.1109\/ISBI48211.2021.9433905"},{"key":"9284_CR107","doi-asserted-by":"crossref","unstructured":"Li J, Yang S, Huang X, Da Q, Yang X, Hu Z, Duan Q, Wang C, Li H (2019) Signet ring cell detection with a semi-supervised learning framework. In: Proceeding of ICIPMI 2019, pp 842\u2013854","DOI":"10.1007\/978-3-030-20351-1_66"},{"key":"9284_CR108","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana K, Pluim J, Chen H, Qi X, Heng P, Guo Y, Wang L, Matuszewski B, Bruni E, Sanchez U (2017) Gland segmentation in colon histology images: the glas challenge contest. Med Image Anal 35:489\u2013502","journal-title":"Med Image Anal"},{"key":"9284_CR109","unstructured":"Zhang J, Jin Y, Xu J, Xu X, Zhang Y (2018) Mdu-net: Multi-scale densely connected u-net for biomedical image segmentation. arXiv:1812.00352"},{"key":"9284_CR110","doi-asserted-by":"crossref","unstructured":"Liu Y, Treible W, Kolagunda A, Nedo A, Saponaro P, Caplan J, Kambhamettu C (2018) Densely connected stacked u-network for filament segmentation in microscopy images. In: Proceeding of ECCV 2018","DOI":"10.1007\/978-3-030-11024-6_30"},{"issue":"3","key":"9284_CR111","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1364\/OSAC.2.000677","volume":"2","author":"C Wu","year":"2019","unstructured":"Wu C, Xie Y, Shao L, Yang J, Ai D, Song H, Wang Y, Huang Y (2019) Automatic boundary segmentation of vascular doppler optical coherence tomography images based on cascaded U-net architecture. OSA Contin 2(3):677\u2013689","journal-title":"OSA Contin"},{"key":"9284_CR112","doi-asserted-by":"crossref","unstructured":"Zhou Z, RSiddiquee M, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"6","key":"9284_CR113","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou Z, Siddiquee M, Tajbakhsh N, Liang J (2019) Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856\u20131867","journal-title":"IEEE Trans Med Imaging"},{"key":"9284_CR114","doi-asserted-by":"crossref","unstructured":"Wang H, Li Y, Luo Z (2020) An Improved Breast Cancer Nuclei Segmentation Method Based on UNet++. In: Proceeding of ICCAI 2020, pp 193\u2013197","DOI":"10.1145\/3404555.3404577"},{"key":"9284_CR115","doi-asserted-by":"crossref","first-page":"29","DOI":"10.4103\/2153-3539.186902","volume":"7","author":"A Janowczyk","year":"2016","unstructured":"Janowczyk A, Madabhushi A (2016) Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J Pathol Inform 7:29","journal-title":"J Pathol Inform"},{"key":"9284_CR116","doi-asserted-by":"crossref","unstructured":"Tsunomura M, Shishikura M, Ishii T, Takahashi R, Tsumura N (2020) Segmentation of microscopic image of colorants using u-net based deep convolutional networks for material appearance design. In: International conference on image and signal processing, pp 197\u2013204","DOI":"10.1007\/978-3-030-51935-3_21"},{"key":"9284_CR117","doi-asserted-by":"crossref","unstructured":"Xu X, Tan T, Xu F (2018) An improved U-net architecture for simultaneous arteriole and venule segmentation in fundus image. In: Proceeding of ACMIUA 2018, pp 333\u2013340","DOI":"10.1007\/978-3-319-95921-4_31"},{"key":"9284_CR118","volume":"1631","author":"X Liang","year":"2020","unstructured":"Liang X, Wang B (2020) Wheat powdery mildew spore images segmentation based on U-Net. J Phys Conf Series 1631:012074","journal-title":"J Phys Conf Series"},{"key":"9284_CR119","doi-asserted-by":"crossref","unstructured":"Patel G, Tekchandani H, Verma S (2019) Cellular segmentation of bright-field absorbance images using residual u-net. In: Proceeding of ICAC 2019, pp 1\u20135","DOI":"10.1109\/ICAC347590.2019.9036737"},{"issue":"1","key":"9284_CR120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-49431-3","volume":"9","author":"E G\u00f3mez de Mariscal","year":"2019","unstructured":"G\u00f3mez de Mariscal E, Ma\u0161ka M, Kotrbov\u00e1 A, Posp\u00edchalov\u00e1 V, Matula P, Mu\u00f1oz-Barrutia A (2019) Deep-learning-based segmentation of small extracellular vesicles in transmission electron microscopy images. Sci Rep 9(1):1\u201310","journal-title":"Sci Rep"},{"key":"9284_CR121","unstructured":"Quan T, Hildebrand D, Jeong W (2016) Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics. arXiv:1612.05360"},{"key":"9284_CR122","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3389\/fnana.2015.00142","volume":"9","author":"I Arganda-Carreras","year":"2015","unstructured":"Arganda-Carreras I, Turaga S, Berger D, Cire\u015fan D, Giusti A, Gambardella L, Schmidhuber J, Laptev D, Dwivedi S, Buhmann J (2015) Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat 9:142","journal-title":"Front Neuroanat"},{"key":"9284_CR123","doi-asserted-by":"crossref","unstructured":"Mehta S, Mercan E, Bartlett J, Weaver D, Elmore JG, Shapiro L (2018) Y-Net: joint segmentation and classification for diagnosis of breast biopsy images. In: Proceeding of ICMICCAI 2018, pp 893\u2013901","DOI":"10.1007\/978-3-030-00934-2_99"},{"key":"9284_CR124","unstructured":"Ke R, Bugeau A, Papadakis N, Schuetz P, Sch\u00f6nlieb C (2019) A multi-task U-net for segmentation with lazy labels. arXiv preprint arXiv:1906.12177"},{"key":"9284_CR125","doi-asserted-by":"crossref","first-page":"104449","DOI":"10.1016\/j.compbiomed.2021.104449","volume":"134","author":"Zekun Wang","year":"2021","unstructured":"Wang Zekun, Zou Yanni, Liu Peter X (2021) Hybrid dilation and attention residual U-Net for medical image segmentation. Comput Biol Med 134:104449","journal-title":"Comput Biol Med"},{"key":"9284_CR126","doi-asserted-by":"crossref","first-page":"104699","DOI":"10.1016\/j.compbiomed.2021.104699","volume":"136","author":"Hasib Zunair","year":"2021","unstructured":"Zunair Hasib, Hamza A Ben (2021) Sharp U-Net: Depthwise convolutional network for biomedical image segmentation. Comput Biol Med 136:104699","journal-title":"Comput Biol Med"},{"issue":"3","key":"9284_CR127","first-page":"1","volume":"17","author":"Zhenzhen Yang","year":"2021","unstructured":"Yang Zhenzhen, Pengfei Xu, Yang Yongpeng, Bao Bing-Kun (2021) A densely connected network based on U-Net for medical image segmentation. ACM Trans Multimed Comput Commun Appl 17(3):1\u201314","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"9284_CR128","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman M (2020) MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387","journal-title":"Neural Netw"},{"key":"9284_CR129","doi-asserted-by":"crossref","unstructured":"Coelho LP, Shariff A, Murphy RF (2009) Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms. In: Proceeding of ISBI 2009, pp 518\u2013521","DOI":"10.1109\/ISBI.2009.5193098"},{"key":"9284_CR130","doi-asserted-by":"crossref","unstructured":"Lou A, Guan S, Loew M (2021) DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation. In: medical imaging 2021: image processing, vol 11596, pp 115962T","DOI":"10.1117\/12.2582338"},{"issue":"2","key":"9284_CR131","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3390\/diagnostics10020110","volume":"10","author":"P Gadosey","year":"2020","unstructured":"Gadosey P, Li Y, Adjei E, Zhang T, Liu Z, Yamak P, Essaf F (2020) Sd-unet: stripping down u-net for segmentation of biomedical images on platforms with low computational budgets. Diagnostics 10(2):110","journal-title":"Diagnostics"},{"key":"9284_CR132","doi-asserted-by":"crossref","unstructured":"Arbelle A, Raviv T (2019) Microscopy cell segmentation via convolutional LSTM networks. In: Proceedimg of ISBI 2019, pp 1008\u20131012","DOI":"10.1109\/ISBI.2019.8759447"},{"issue":"4","key":"9284_CR133","doi-asserted-by":"crossref","first-page":"045004","DOI":"10.1088\/2632-2153\/aba8e8","volume":"1","author":"H Abdallah","year":"2020","unstructured":"Abdallah H, Formosa B, Liyanaarachchi A, Saigh M, Silvers S, Arslanturk S, Taatjes D, Larsson L, Jena B, Gatti D (2020) Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images. Mach Learn Sci Technol 1(4):045004","journal-title":"Mach Learn Sci Technol"},{"issue":"7","key":"9284_CR134","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A (2017) A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging 36(7):1550\u20131560","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"9284_CR135","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/JBHI.2018.2852635","volume":"23","author":"A Chakravarty","year":"2018","unstructured":"Chakravarty A, Sivaswamy J (2018) RACE-net: a recurrent neural network for biomedical image segmentation. IEEE J Biomed Health Inform 23(3):1151\u20131162","journal-title":"IEEE J Biomed Health Inform"},{"key":"9284_CR136","doi-asserted-by":"crossref","unstructured":"Alom M, Hasan M, Yakopcic C, Taha T, Asari V (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv:1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"1","key":"9284_CR137","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","volume":"6","author":"M Alom","year":"2019","unstructured":"Alom M, Yakopcic C, Hasan M, Taha T, Asari V (2019) Recurrent residual U-Net for medical image segmentation. J Med Imaging 6(1):014006","journal-title":"J Med Imaging"},{"key":"9284_CR138","doi-asserted-by":"crossref","unstructured":"Alom M, Yakopcic C, Taha T, Asari V (2018) Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net). In: Proceeding of NAECON 2018, pp 228\u2013233","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"9284_CR139","unstructured":"Zahangir A, Yakopcic C, Taha T, Asari V (2018) microscopic nuclei classification, segmentation and detection with improved deep convolutional neural network (DCNN) approaches. arXiv e-prints, pages arXiv\u20131811"},{"issue":"2","key":"9284_CR140","doi-asserted-by":"crossref","first-page":"e201900203","DOI":"10.1002\/jbio.201900203","volume":"13","author":"Q Yang","year":"2020","unstructured":"Yang Q, Xu Z, Liao C, Cai J, Huang Y, Chen H, Tao X, Huang Z, Chen J, Dong J (2020) Epithelium segmentation and automated Gleason grading of prostate cancer via deep learning in label-free multiphoton microscopic images. J Biophotonics 13(2):e201900203","journal-title":"J Biophotonics"},{"key":"9284_CR141","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"9284_CR142","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceeding of AAAI, vol 31, pp 2017","DOI":"10.1609\/aaai.v31i1.11231"},{"issue":"10","key":"9284_CR143","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: Context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"9284_CR144","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yin F, Liu J, Wong W, Tan N, Lee B, Cheng J, Wong T (2010) Origa-light: an online retinal fundus image database for glaucoma analysis and research. In: Proceeding of AICEMB 2010, pp 3065\u20133068","DOI":"10.1109\/IEMBS.2010.5626137"},{"key":"9284_CR145","doi-asserted-by":"crossref","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","volume":"192","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Wu C, Coleman S, Kerr D (2020) DENSE-INception U-net for medical image segmentation. Comput Methods Progr Biomed 192:105395","journal-title":"Comput Methods Progr Biomed"},{"key":"9284_CR146","doi-asserted-by":"crossref","unstructured":"Huang Y, Li X, Yan C, Liu L, Dai H (2020) MIRD-Net for Medical Image Segmentation. In: Pacific-Asia conference on knowledge discovery and data mining, pp 207\u2013219","DOI":"10.1007\/978-3-030-47436-2_16"},{"key":"9284_CR147","doi-asserted-by":"crossref","unstructured":"Trimeche I, Rossant F, Bloch I, P\u00e2ques M (2020) Fully automatic CNN-based segmentation of retinal bifurcations in 2D adaptive optics ophthalmoscopy images. In: Proceeding of IPTA 2020, pp 1\u20136","DOI":"10.1109\/IPTA50016.2020.9286662"},{"issue":"1","key":"9284_CR148","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.bbe.2021.12.010","volume":"42","author":"Ao Chen","year":"2022","unstructured":"Chen A, Li C, Zou S, Rahaman Md M, Yao Y, Chen H, Yang H, Zhao P, Weiming H, Liu W et al (2022) SVIA dataset: a new dataset of microscopic videos and images for computer-aided sperm analysis. Biocybern Biomed Eng 42(1):204\u2013214","journal-title":"Biocybern Biomed Eng"},{"key":"9284_CR149","doi-asserted-by":"crossref","first-page":"829027","DOI":"10.3389\/fmicb.2022.829027","volume":"13","author":"Peng Zhao","year":"2022","unstructured":"Zhao P, Li C, Rahaman Md M, Hao X, Ma P, Yang H, Sun H, Jiang T, Ning X, Grzegorzek M (2022) Emds-6: environmental microorganism image dataset sixth version for image denoising, segmentation, feature extraction, classification, and detection method evaluation. Front Microbiol 13:829027","journal-title":"Front Microbiol"},{"key":"9284_CR150","doi-asserted-by":"crossref","first-page":"1084312","DOI":"10.3389\/fmicb.2023.1084312","volume":"14","author":"Hechen Yang","year":"2023","unstructured":"Yang H, Li C, Zhao X, Cai B, Zhang J, Ma P, Zhao P, Chen A, Jiang T, Sun H et al (2023) EMDS-7: environmental microorganism image dataset seventh version for multiple object detection evaluation. Front Microbiol 14:1084312","journal-title":"Front Microbiol"},{"key":"9284_CR151","doi-asserted-by":"crossref","first-page":"102534","DOI":"10.1016\/j.ejmp.2023.102534","volume":"107","author":"Hu Weiming","year":"2023","unstructured":"Weiming H, Li C, Rahaman Md M, Chen H, Liu W, Yao Y, Sun H, Grzegorzek M, Li X, 2023) EBHI: a new enteroscope biopsy histopathological H &E image dataset for image classification evaluation. Phys Med 107:102534","journal-title":"Phys Med"},{"key":"9284_CR152","doi-asserted-by":"crossref","first-page":"1114673","DOI":"10.3389\/fmed.2023.1114673","volume":"10","author":"Liyu Shi","year":"2023","unstructured":"Shi L, Li X, Weiming H, Chen H, Chen J, Fan Z, Gao M, Jing Y, Guotao L, Ma D et al (2023) EBHI-Seg: a novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks. Front Med 10:1114673","journal-title":"Front Med"},{"key":"9284_CR153","doi-asserted-by":"crossref","first-page":"105207","DOI":"10.1016\/j.compbiomed.2021.105207","volume":"142","author":"Hu Weiming","year":"2022","unstructured":"Weiming H, Li C, Li X, Rahaman Md M, Ma J, Zhang Y, Chen H, Liu W, Sun C, Yao Y et al (2022) GasHisSDB: a new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. Comput Biol Med 142:105207","journal-title":"Comput Biol Med"},{"key":"9284_CR154","doi-asserted-by":"crossref","unstructured":"Piantadosi G, Sansone M, Sansone C (2018) Breast segmentation in mri via u-net deep convolutional neural networks. In: Proceeding of ICPR 2018, pp 3917\u20133922","DOI":"10.1109\/ICPR.2018.8545327"},{"key":"9284_CR155","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang R, Zheng L, Meng C, Biswal B (2019) 3d u-net based brain tumor segmentation and survival days prediction. In: Proceeding of MICCAI-BW 2019, pp 131\u2013141","DOI":"10.1007\/978-3-030-46640-4_13"},{"issue":"8","key":"9284_CR156","doi-asserted-by":"crossref","first-page":"e0236493","DOI":"10.1371\/journal.pone.0236493","volume":"15","author":"B Lee","year":"2020","unstructured":"Lee B, Yamanakkanavar N, Choi J (2020) Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture. PLoS ONE 15(8):e0236493","journal-title":"PLoS ONE"},{"key":"9284_CR157","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","volume":"365","author":"L Rundo","year":"2019","unstructured":"Rundo L, Han C, Nagano Y, Zhang J, Hataya R, Militello C, Tangherloni A, Nobile M, Ferretti C, Besozzi D (2019) USE-Net: incorporating squeeze-and-excitation blocks into U-net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365:31\u201343","journal-title":"Neurocomputing"},{"key":"9284_CR158","doi-asserted-by":"crossref","unstructured":"Kermi A, Mahmoudi I, Khadir M (2018) Deep convolutional neural networks using U-Net for automatic brain tumor segmentation in multimodal MRI volumes. In: Proceeding of MICCAI-BW 2018, pp 37\u201348","DOI":"10.1007\/978-3-030-11726-9_4"},{"key":"9284_CR159","doi-asserted-by":"crossref","unstructured":"Chen Y, Cao Z, Cao C, Yang J, Zhang J (2018) A modified U-Net for brain Mr image segmentation. In: Proceeding of ICCCS 2018, pp 233\u2013242","DOI":"10.1007\/978-3-030-00021-9_22"},{"key":"9284_CR160","doi-asserted-by":"crossref","first-page":"166823","DOI":"10.1109\/ACCESS.2019.2953934","volume":"7","author":"T Song","year":"2019","unstructured":"Song T, Meng F, Rodriguez-Paton A, Li P, Zheng P, Wang X (2019) U-next: a novel convolution neural network with an aggregation u-net architecture for gallstone segmentation in CT images. IEEE Access 7:166823\u2013166832","journal-title":"IEEE Access"},{"key":"9284_CR161","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","volume":"66","author":"H Roth","year":"2018","unstructured":"Roth H, Oda H, Zhou X, Shimizu N, Yang Y, Hayashi Y, Oda M, Fujiwara M, Misawa K, Mori K (2018) An application of cascaded 3D fully convolutional networks for medical image segmentation. Comput Med Imaging Graph 66:90\u201399","journal-title":"Comput Med Imaging Graph"},{"issue":"2","key":"9284_CR162","first-page":"63","volume":"36","author":"H Roth","year":"2018","unstructured":"Roth H, Shen C, Oda H, Oda M, Hayashi Y, Misawa K, Mori K (2018) Deep learning and its application to medical image segmentation. Med Imaging Technol 36(2):63\u201371","journal-title":"Med Imaging Technol"},{"key":"9284_CR163","unstructured":"Chen Y, Lin Y, Wang C, Lee C, Lee W, Wang T, Chen C (2019) Coronary artery segmentation in cardiac CT angiography using 3D multi-channel U-net. arXiv:1907.12246"},{"key":"9284_CR164","doi-asserted-by":"crossref","first-page":"100007","DOI":"10.1016\/j.cmpbup.2021.100007","volume":"1","author":"N Saeedizadeh","year":"2021","unstructured":"Saeedizadeh N, Minaee S, Kafieh R, Yazdani S, Sonka M (2021) COVID TV-Unet: Segmenting COVID-19 chest CT images using connectivity imposed Unet. Comput Methods Progr Biomed Update 1:100007","journal-title":"Comput Methods Progr Biomed Update"},{"key":"9284_CR165","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","volume":"7","author":"Y Weng","year":"2019","unstructured":"Weng Y, Zhou T, Li Y, Qiu X (2019) Nas-unet: neural architecture search for medical image segmentation. IEEE Access 7:44247\u201344257","journal-title":"IEEE Access"},{"key":"9284_CR166","doi-asserted-by":"crossref","unstructured":"Almajalid R, Shan J, Du Y, Zhang M (2018) Development of a deep-learning-based method for breast ultrasound image segmentation. In: Proceeding of ICMLA 2018, pp 1103\u20131108","DOI":"10.1109\/ICMLA.2018.00179"},{"key":"9284_CR167","doi-asserted-by":"crossref","unstructured":"Wang Y, Wei C, Wang Z, Lu Q, Wang C (2018) A more streamlined u-net for nerve segmentation in ultrasound images. In: Proceeding of CAC 2018, pp 101\u2013104","DOI":"10.1109\/CAC.2018.8623052"},{"issue":"7","key":"9284_CR168","doi-asserted-by":"crossref","first-page":"075015","DOI":"10.1088\/1361-6560\/ab0ef4","volume":"64","author":"X Li","year":"2019","unstructured":"Li X, Hong Y, Kong D, Zhang X (2019) Automatic segmentation of levator hiatus from ultrasound images using U-net with dense connections. Phys Med Biol 64(7):075015","journal-title":"Phys Med Biol"},{"key":"9284_CR169","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ultras.2019.03.014","volume":"96","author":"J Yang","year":"2019","unstructured":"Yang J, Faraji M, Basu A (2019) Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. Ultrasonics 96:24\u201333","journal-title":"Ultrasonics"},{"issue":"6","key":"9284_CR170","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11548-020-02158-3","volume":"15","author":"M Amiri","year":"2020","unstructured":"Amiri M, Brooks R, Behboodi B, Rivaz H (2020) Two-stage ultrasound image segmentation using U-Net and test time augmentation. Int J Comput Assist Radiol Surg 15(6):981\u2013988","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"9284_CR171","doi-asserted-by":"crossref","unstructured":"Amiri M, Brooks R, Rivaz H (2019) Fine tuning u-net for ultrasound image segmentation: Which layers? In: Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, pp 235\u2013242","DOI":"10.1007\/978-3-030-33391-1_27"},{"key":"9284_CR172","doi-asserted-by":"crossref","unstructured":"Wu J, Liu W, Li C, Jiang T, Shariful IM, Sun H, Li X, Li X, Huang X, Grzegorzek M (2022) A state-of-the-art survey of u-net in microscopic image analysis: from simple usage to structure mortification. arXiv:2202.06465","DOI":"10.1007\/s00521-023-09284-4"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09284-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09284-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09284-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T12:15:44Z","timestamp":1707480944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09284-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,10]]},"references-count":172,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9284"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09284-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,10]]},"assertion":[{"value":"10 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"The authors declare that they have no conflicts of interest in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}