ArticleBiomedical optics express2021
Deep-learning-assisted microscopy with ultraviolet surface excitation for rapid slide-free histological imaging.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Paired CycleGAN-based virtual staining for 3D X-ray histology of bone-implant systems.Journal of synchrotron radiation · 2026Article
- Ultraviolet Sensing-Guided Biomedical Systems: From Label-Free Imaging to Dosimetry and Therapy Feedback.Biosensors · 2026Review
- Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images.NPJ breast cancer · 2026Article
- Rapid Arbitrary-Shape Microscopy of Unsectioned Tissues for Precise Intraoperative Tumor Margin Assessment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Fast and label-free 3D virtual H&E histology via active phase modulation-assisted dynamic full-field OCT.Npj imaging · 2025Article
- Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.International journal of computer assisted radiology and surgery · 2025Article
- Widefield frequency-domain autofluorescence lifetime imaging for detecting breast cancer in murine xenograft tumor tissues.Journal of biomedical optics · 2025Article
- Single color digital H&E staining with In-and-Out Net.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.Nature communications · 2024Article
- An end-to-end workflow for nondestructive 3D pathology.Nature protocols · 2024Review
- Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining.Biomedical optics express · 2024Article
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- Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy.Nature communications · 2023Article
- An end-to-end workflow for non-destructive 3D pathology.bioRxiv : the preprint server for biology · 2023Article
- Deep learning-enabled virtual histological staining of biological samples.Light, science & applications · 2023Review
- Rapid and label-free histological imaging of unprocessed surgical tissues via dark-field reflectance ultraviolet microscopy.iScience · 2023Article
- Flow chamber staining modality for real-time inspection of dynamic phenotypes in multiple histological stains.PloS one · 2023Article
- Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning.BME frontiers · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathological examination of tissue sections is the gold standard for disease diagnosis. However, the conventional histopathology workflow requires lengthy and laborious sample preparation to obtain thin tissue slices, causing about a one-week delay to generate an accurate diagnostic report. Recently, microscopy with ultraviolet surface excitation (MUSE), a rapid and slide-free imaging technique, has been developed to image fresh and thick tissues with specific molecular contrast. Here, we propose to apply an unsupervised generative adversarial network framework to translate colorful MUSE images into Deep-MUSE images that highly resemble hematoxylin and eosin staining, allowing easy adaptation by pathologists. By eliminating the needs of all sample processing steps (except staining), a MUSE image with subcellular resolution for a typical brain biopsy (5 mm × 5 mm) can be acquired in 5 minutes, which is further translated into a Deep-MUSE image in 40 seconds, simplifying the standard histopathology workflow dramatically and providing histological images intraoperatively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.