{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:03:35Z","timestamp":1784073815758,"version":"3.55.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":68,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFA1801100"],"award-info":[{"award-number":["2023YFA1801100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholars Program of Shandong Province","award":["tstp20231250"],"award-info":[{"award-number":["tstp20231250"]}]},{"name":"Project of Chronic Disease Management with Integrated Traditional Chinese and Modern Medicine","award":["CXZH2024066"],"award-info":[{"award-number":["CXZH2024066"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"DOI":"10.1038\/s41597-026-07418-6","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T08:15:11Z","timestamp":1778228111000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization"],"prefix":"10.1038","volume":"13","author":[{"given":"Qincheng","family":"Qiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2045-1290","authenticated-orcid":false,"given":"Xinguo","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"7418_CR1","doi-asserted-by":"publisher","first-page":"4457","DOI":"10.1364\/boe.578730","volume":"16","author":"K Marcelino","year":"2025","unstructured":"Marcelino, K. et al. Portable in vivo confocal ophthalmoscope for non-contact imaging of the cornea and anterior segment of the eye. Biomedical optics express 16, 4457\u20134469, https:\/\/doi.org\/10.1364\/boe.578730 (2025).","journal-title":"Biomedical optics express"},{"key":"7418_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102188","volume":"73","author":"R Herrera-Pereda","year":"2021","unstructured":"Herrera-Pereda, R., Taboada Crispi, A., Babin, D., Philips, W. & Holsbach Costa, M. A Review On digital image processing techniques for in-Vivo confocal images of the cornea. Medical image analysis 73, 102188, https:\/\/doi.org\/10.1016\/j.media.2021.102188 (2021).","journal-title":"Medical image analysis"},{"key":"7418_CR3","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/tbme.2016.2573642","volume":"64","author":"X Chen","year":"2017","unstructured":"Chen, X. et al. An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images. IEEE transactions on bio-medical engineering 64, 786\u2013794, https:\/\/doi.org\/10.1109\/tbme.2016.2573642 (2017).","journal-title":"IEEE transactions on bio-medical engineering"},{"key":"7418_CR4","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241307573","volume":"10","author":"Q Qiao","year":"2024","unstructured":"Qiao, Q. et al. Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy. Digital health 10, 20552076241307573, https:\/\/doi.org\/10.1177\/20552076241307573 (2024).","journal-title":"Digital health"},{"key":"7418_CR5","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1167\/tvst.14.9.29","volume":"14","author":"Q Qiao","year":"2025","unstructured":"Qiao, Q., Cao, J. & Hou, X. Development of Diagnostic Nomograms Using Corneal Nerve Parameters for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus. Translational vision science & technology 14, 29, https:\/\/doi.org\/10.1167\/tvst.14.9.29 (2025).","journal-title":"Translational vision science & technology"},{"key":"7418_CR6","doi-asserted-by":"publisher","first-page":"e1680","DOI":"10.1212\/wnl.0000000000206902","volume":"100","author":"SS Gylfadottir","year":"2023","unstructured":"Gylfadottir, S. S. et al. Assessing Corneal Confocal Microscopy and Other Small Fiber Measures in Diabetic Polyneuropathy. Neurology 100, e1680\u2013e1690, https:\/\/doi.org\/10.1212\/wnl.0000000000206902 (2023).","journal-title":"Neurology"},{"key":"7418_CR7","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1038\/s41531-025-00969-2","volume":"11","author":"HQ Yang","year":"2025","unstructured":"Yang, H. Q. et al. Corneal confocal microscopy differentiates patients with secondary parkinsonism from idiopathic Parkinson\u2019s disease. NPJ Parkinson\u2019s disease 11, 112, https:\/\/doi.org\/10.1038\/s41531-025-00969-2 (2025).","journal-title":"NPJ Parkinson\u2019s disease"},{"key":"7418_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1167\/iovs.66.6.21","volume":"66","author":"D Li","year":"2025","unstructured":"Li, D. et al. Cornea Nerves Can Identify Different Types of Parkinson\u2019s Disease. Investigative ophthalmology & visual science 66, 21, https:\/\/doi.org\/10.1167\/iovs.66.6.21 (2025).","journal-title":"Investigative ophthalmology & visual science"},{"key":"7418_CR9","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jtos.2025.08.002","volume":"38","author":"J Cheng","year":"2025","unstructured":"Cheng, J. et al. Exploration of imaging and molecular biomarkers for differentiation of neuropathic corneal pain from dry eye syndrome. The ocular surface 38, 230\u2013241, https:\/\/doi.org\/10.1016\/j.jtos.2025.08.002 (2025).","journal-title":"The ocular surface"},{"key":"7418_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-025-01577-3","volume":"8","author":"ND Koseoglu","year":"2025","unstructured":"Koseoglu, N. D. et al. Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy. NPJ digital medicine 8, 277, https:\/\/doi.org\/10.1038\/s41746-025-01577-3 (2025).","journal-title":"NPJ digital medicine"},{"key":"7418_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1167\/tvst.12.12.3","volume":"12","author":"LRM Raasing","year":"2023","unstructured":"Raasing, L. R. M. et al. Fully Automatic, Semiautomatic, and Manual Corneal Nerve Fiber Analysis in Patients With Sarcoidosis. Translational vision science & technology 12, 3, https:\/\/doi.org\/10.1167\/tvst.12.12.3 (2023).","journal-title":"Translational vision science & technology"},{"key":"7418_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.831307","volume":"9","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Wu, Y., Li, W. & Huang, X. Semiautomated and Automated Quantitative Analysis of Corneal Sub-Basal Nerves in Patients With DED With Ocular Pain Using IVCM. Frontiers in medicine 9, 831307, https:\/\/doi.org\/10.3389\/fmed.2022.831307 (2022).","journal-title":"Frontiers in medicine"},{"key":"7418_CR13","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1097\/ico.0000000000003319","volume":"42","author":"ZZ Zemborain","year":"2023","unstructured":"Zemborain, Z. Z. et al. Open-Source Automated Segmentation of Neuronal Structures in Corneal Confocal Microscopy Images of the Subbasal Nerve Plexus With Accuracy on Par With Human Segmentation. Cornea 42, 1309\u20131319, https:\/\/doi.org\/10.1097\/ico.0000000000003319 (2023).","journal-title":"Cornea"},{"key":"7418_CR14","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1002\/cyto.a.20022","volume":"58","author":"E Meijering","year":"2004","unstructured":"Meijering, E. et al. Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry. Part A: the journal of the International Society for Analytical Cytology 58, 167\u2013176, https:\/\/doi.org\/10.1002\/cyto.a.20022 (2004).","journal-title":"Cytometry. Part A: the journal of the International Society for Analytical Cytology"},{"key":"7418_CR15","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.media.2011.05.016","volume":"15","author":"MA Dabbah","year":"2011","unstructured":"Dabbah, M. A., Graham, J., Petropoulos, I. N., Tavakoli, M. & Malik, R. A. Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging. Medical image analysis 15, 738\u2013747, https:\/\/doi.org\/10.1016\/j.media.2011.05.016 (2011).","journal-title":"Medical image analysis"},{"key":"7418_CR16","doi-asserted-by":"publisher","DOI":"10.1111\/ene.70396","volume":"32","author":"IN Petropoulos","year":"2025","unstructured":"Petropoulos, I. N. et al. Corneal Confocal Microscopy to Diagnose Peripheral Neuropathy: A Systematic Review and Meta-Analysis. European journal of neurology 32, e70396, https:\/\/doi.org\/10.1111\/ene.70396 (2025).","journal-title":"European journal of neurology"},{"key":"7418_CR17","doi-asserted-by":"publisher","unstructured":"Tang, W. et al. Multi-scale and local feature guidance network for corneal nerve fiber segmentation. Physics in medicine and biology 68, https:\/\/doi.org\/10.1088\/1361-6560\/acccd0 (2023).","DOI":"10.1088\/1361-6560\/acccd0"},{"key":"7418_CR18","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/jbhi.2021.3094520","volume":"26","author":"C Yang","year":"2022","unstructured":"Yang, C. et al. Multi-Discriminator Adversarial Convolutional Network for Nerve Fiber Segmentation in Confocal Corneal Microscopy Images. IEEE journal of biomedical and health informatics 26, 648\u2013659, https:\/\/doi.org\/10.1109\/jbhi.2021.3094520 (2022).","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"7418_CR19","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1167\/tvst.9.2.32","volume":"9","author":"S Wei","year":"2020","unstructured":"Wei, S., Shi, F., Wang, Y., Chou, Y. & Li, X. A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy. Translational vision science & technology 9, 32, https:\/\/doi.org\/10.1167\/tvst.9.2.32 (2020).","journal-title":"Translational vision science & technology"},{"key":"7418_CR20","doi-asserted-by":"publisher","unstructured":"Yu, K. et al. Segmentation and multiparametric evaluation of corneal whorl-like nerves for in vivo confocal microscopy images in dry eye disease. BMJ open ophthalmology 9, https:\/\/doi.org\/10.1136\/bmjophth-2024-001861 (2024).","DOI":"10.1136\/bmjophth-2024-001861"},{"key":"7418_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103666","volume":"119","author":"T Salahuddin","year":"2020","unstructured":"Salahuddin, T. & Qidwai, U. Computational methods for automated analysis of corneal nerve images: Lessons learned from retinal fundus image analysis. Computers in biology and medicine 119, 103666, https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103666 (2020).","journal-title":"Computers in biology and medicine"},{"key":"7418_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1167\/tvst.11.6.24","volume":"11","author":"MAK Setu","year":"2022","unstructured":"Setu, M. A. K., Schmidt, S., Musial, G., Stern, M. E. & Steven, P. Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning. Translational vision science & technology 11, 24, https:\/\/doi.org\/10.1167\/tvst.11.6.24 (2022).","journal-title":"Translational vision science & technology"},{"key":"7418_CR23","doi-asserted-by":"publisher","unstructured":"Li, H. et al. Progressive Distillation for Incremental Learning in Corneal Confocal Microscopy Segmentation. IEEE transactions on medical imaging Pp, https:\/\/doi.org\/10.1109\/tmi.2025.3593472 (2025).","DOI":"10.1109\/tmi.2025.3593472"},{"key":"7418_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s40662-020-00192-5","volume":"7","author":"JD Oakley","year":"2020","unstructured":"Oakley, J. D. et al. Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images. Eye and vision (London, England) 7, 27, https:\/\/doi.org\/10.1186\/s40662-020-00192-5 (2020).","journal-title":"Eye and vision (London, England)"},{"key":"7418_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110051","volume":"190","author":"L Liu","year":"2025","unstructured":"Liu, L. et al. A lightweight PCT-Net for segmenting neural fibers in low-quality CCM images. Computers in biology and medicine 190, 110051, https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110051 (2025).","journal-title":"Computers in biology and medicine"},{"key":"7418_CR26","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s00125-019-05023-4","volume":"63","author":"BM Williams","year":"2020","unstructured":"Williams, B. M. et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia 63, 419\u2013430, https:\/\/doi.org\/10.1007\/s00125-019-05023-4 (2020).","journal-title":"Diabetologia"},{"key":"7418_CR27","doi-asserted-by":"publisher","unstructured":"iMed(Ningbo Institute of Materials Technology and Engineering, C. A. o. S., Ningbo, China),. CORN: Corneal nerve fiber dataset, https:\/\/doi.org\/10.5281\/zenodo.12776091 (2024).","DOI":"10.5281\/zenodo.12776091"},{"key":"7418_CR28","doi-asserted-by":"publisher","unstructured":"iMED-Ningbo. CORN-Pro, https:\/\/doi.org\/10.5281\/zenodo.14263883 (2024).","DOI":"10.5281\/zenodo.14263883"},{"key":"7418_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106303","volume":"151","author":"G Li","year":"2022","unstructured":"Li, G., Li, T., Li, F. & Zhang, C. NerveStitcher: Corneal confocal microscope images stitching with neural networks. Computers in biology and medicine 151, 106303, https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106303 (2022).","journal-title":"Computers in biology and medicine"},{"key":"7418_CR30","doi-asserted-by":"publisher","unstructured":"Lagali, N. et al. Wide field SBP mosaics, https:\/\/doi.org\/10.6084\/m9.figshare.5548315.v1 (2018).","DOI":"10.6084\/m9.figshare.5548315.v1"},{"key":"7418_CR31","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/4913031","volume":"2022","author":"J Cao","year":"2022","unstructured":"Cao, J. et al. Corneal Nerve Parameter Reference Values for Chinese Adults Assessed by Corneal Confocal Microscopy. Journal of diabetes research 2022, 4913031, https:\/\/doi.org\/10.1155\/2022\/4913031 (2022).","journal-title":"Journal of diabetes research"},{"key":"7418_CR32","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8883736","volume":"2020","author":"A Zuo","year":"2020","unstructured":"Zuo, A. et al. The Association of Fasting C-peptide with Corneal Neuropathy in Patients with Type 2 Diabetes. Journal of diabetes research 2020, 8883736, https:\/\/doi.org\/10.1155\/2020\/8883736 (2020).","journal-title":"Journal of diabetes research"},{"key":"7418_CR33","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1364\/boe.562924","volume":"16","author":"Q Qiao","year":"2025","unstructured":"Qiao, Q. & Hou, X. ConNeCT: weakly supervised corneal confocal microscopy image inpainting network based on a diffusion model. Biomedical optics express 16, 2615\u20132630, https:\/\/doi.org\/10.1364\/boe.562924 (2025).","journal-title":"Biomedical optics express"},{"key":"7418_CR34","unstructured":"Wada, K. Labelme: Image Polygonal Annotation with Python. Github repository, https:\/\/github.com\/wkentaro\/labelme (2025)."},{"key":"7418_CR35","doi-asserted-by":"publisher","unstructured":"Qiao, Q. An Annotated Corneal Confocal Microscopy Dataset for Nerve Segmentation and Clinical Characterization, https:\/\/doi.org\/10.5281\/zenodo.17570504 (2026).","DOI":"10.5281\/zenodo.17570504"},{"key":"7418_CR36","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1167\/tvst.14.11.27","volume":"14","author":"Q Qiao","year":"2025","unstructured":"Qiao, Q., Ren, T., Chen, L. & Hou, X. SuperCCM: An Open Source Python Toolkit for Automated Quantification of Corneal Nerve Fibers in Confocal Microscopy Images. Translational vision science & technology 14, 27, https:\/\/doi.org\/10.1167\/tvst.14.11.27 (2025).","journal-title":"Translational vision science & technology"},{"key":"7418_CR37","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.jtos.2024.08.002","volume":"34","author":"RE Ruiz-Lozano","year":"2024","unstructured":"Ruiz-Lozano, R. E. et al. Deep-learning based analysis of in-vivo confocal microscopy images of the subbasal corneal nerve plexus\u2019 inferior whorl in patients with neuropathic corneal pain and dry eye disease. The ocular surface 34, 241\u2013246, https:\/\/doi.org\/10.1016\/j.jtos.2024.08.002 (2024).","journal-title":"The ocular surface"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07418-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07418-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07418-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T23:09:16Z","timestamp":1784070556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07418-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["7418"],"URL":"https:\/\/doi.org\/10.1038\/s41597-026-07418-6","relation":{},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"assertion":[{"value":"11 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"1051"}}