气候变化领域集成服务门户(CCPortal): Deep learning extended depth-of-field microscope for fast and slide-free histology

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DOI	10.1073/PNAS.2013571117
	Deep learning extended depth-of-field microscope for fast and slide-free histology
	Jin L.; Tang Y.; Wu Y.; Coole J.B.; Tan M.T.; Zhao X.; Badaoui H.; Robinson J.T.; Williams M.D.; Gillenwater A.M.; Richards-Kortum R.R.; Veeraraghavan A.
发表日期	2021
ISSN	00278424
起始页码	33051
结束页码	33060
卷号	117 期号:52
英文摘要	Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 μm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Deep learning; End-to-end optimization; Extended depth-of-field microscopy; Pathology; Phase mask
语种	英语
scopus关键词	Article; controlled study; deep learning; depth perception; diagnostic procedure; diffraction; histology; human; human tissue; image analysis; light adaptation; oral surgery; peroperative care; physical parameters; priority journal; process optimization; reconstruction algorithm; sampling; surgical margin; tissue repair; tissue structure; algorithm; animal; biopsy; calibration; carcinoma; devices; fluorescence microscopy; image processing; mouth tumor; pathology; pig; procedures; Algorithms; Animals; Biopsy; Calibration; Carcinoma; Deep Learning; Humans; Image Processing, Computer-Assisted; Microscopy, Fluorescence; Mouth Neoplasms; Swine
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179670
作者单位	Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States; Department of Applied Physics, Rice University, Houston, TX 77005, United States; Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
推荐引用方式 GB/T 7714	Jin L.,Tang Y.,Wu Y.,et al. Deep learning extended depth-of-field microscope for fast and slide-free histology[J],2021,117(52).
APA	Jin L..,Tang Y..,Wu Y..,Coole J.B..,Tan M.T..,...&Veeraraghavan A..(2021).Deep learning extended depth-of-field microscope for fast and slide-free histology.Proceedings of the National Academy of Sciences of the United States of America,117(52).
MLA	Jin L.,et al."Deep learning extended depth-of-field microscope for fast and slide-free histology".Proceedings of the National Academy of Sciences of the United States of America 117.52(2021).