DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology

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dc.citation.articleNumber2935en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber15en_US
dc.contributor.authorJin, Lingboen_US
dc.contributor.authorTang, Yuboen_US
dc.contributor.authorCoole, Jackson B.en_US
dc.contributor.authorTan, Melody T.en_US
dc.contributor.authorZhao, Xuanen_US
dc.contributor.authorBadaoui, Hawraaen_US
dc.contributor.authorRobinson, Jacob T.en_US
dc.contributor.authorWilliams, Michelle D.en_US
dc.contributor.authorVigneswaran, Nadarajahen_US
dc.contributor.authorGillenwater, Ann M.en_US
dc.contributor.authorRichards-Kortum, Rebecca R.en_US
dc.contributor.authorVeeraraghavan, Ashoken_US
dc.date.accessioned2024-07-25T20:55:17Zen_US
dc.date.available2024-07-25T20:55:17Zen_US
dc.date.issued2024en_US
dc.description.abstractHistopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.en_US
dc.identifier.citationJin, L., Tang, Y., Coole, J. B., Tan, M. T., Zhao, X., Badaoui, H., Robinson, J. T., Williams, M. D., Vigneswaran, N., Gillenwater, A. M., Richards-Kortum, R. R., & Veeraraghavan, A. (2024). DeepDOF-SE: Affordable deep-learning microscopy platform for slide-free histology. Nature Communications, 15(1), 2935. https://doi.org/10.1038/s41467-024-47065-2en_US
dc.identifier.digitals41467-024-47065-2en_US
dc.identifier.doihttps://doi.org/10.1038/s41467-024-47065-2en_US
dc.identifier.urihttps://hdl.handle.net/1911/117521en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDeepDOF-SE: affordable deep-learning microscopy platform for slide-free histologyen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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