ArticleOptica2023
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual hematoxylin and eosin staining.
Article in Optica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Integrative single-cell and spatial transcriptomic approaches to decipher the tumor microenvironment and therapeutic resistance in pancreatic cancer.Cancer biology & therapy · 2026Review
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- High-precision label-free virtual H&E staining of 3D holotomography using DAPI-guided conditional diffusion learning.International journal of computer assisted radiology and surgery · 2026Article
- Qualitative and quantitative assessment ofBiomedical optics express · 2026Article
- Quantitative Stain Mapping in X-Ray Virtual Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence assisted multi-model pathological diagnosis of breast cancer based on multispectral autofluorescence images.NPJ breast cancer · 2026Article
- Single capture quantitative oblique back-illumination microscopy.Npj imaging · 2026Article
- Diagnosis and management of nipple-areolar complex conditions in postmenopausal women: a retrospective study highlighting multidisciplinary care needs.International journal of women's dermatology · 2026Article
- Label-free intraoperative imaging of hemodynamics using deep learning.Biomedical optics express · 2026Article
- Article
- FPM2Stain Net: physics-guided super-resolution and multi-modal virtual staining for digital histopathology.Biomedical optics express · 2026Article
- Deep Learning-Enabled Virtual Multiplexed Immunostaining of Label-Free Tissue for Vascular Invasion Assessment.BME frontiers · 2026Article
- Back-illumination interference tomography for imaging weak scattering in thick tissues.Optica · 2025Article
- Label-free in-line characterization of immune cell culture using quantitative phase imaging.NPJ Regenerative medicine · 2025Article
- Deep UV laser scanning microscopy with integrated 1050 nm spectral-domain optical coherence tomography for multi-contrast tissue imaging.Biomedical optics express · 2025Article
- Virtual staining of label-free tissue in imaging mass spectrometry.Science advances · 2025Article
- Pixel super-resolved virtual staining of label-free tissue using diffusion models.Nature communications · 2025Article
- Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.Nature communications · 2025Article
- Fast and label-free 3D virtual H&E histology via active phase modulation-assisted dynamic full-field OCT.Npj imaging · 2025Article
- Label-free optical microscopy with artificial intelligence: a new paradigm in pathology.Biophotonics discovery · 2025Review
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9 authors.
Funding
Abstract
Histological staining of tissue biopsies, especially hematoxylin and eosin (H&E) staining, serves as the benchmark for disease diagnosis and comprehensive clinical assessment of tissue. However, the typical formalin-fixation, paraffin-embedding (FFPE) process is laborious and time consuming, often limiting its usage in time-sensitive applications such as surgical margin assessment. To address these challenges, we combine an emerging 3D quantitative phase imaging technology, termed quantitative oblique back illumination microscopy (qOBM), with an unsupervised generative adversarial network pipeline to map qOBM phase images of unaltered thick tissues (i.e., label- and slide-free) to virtually stained H&E-like (vH&E) images. We demonstrate that the approach achieves high-fidelity conversions to H&E with subcellular detail using fresh tissue specimens from mouse liver, rat gliosarcoma, and human gliomas. We also show that the framework directly enables additional capabilities such as H&E-like contrast for volumetric imaging. The quality and fidelity of the vH&E images are validated using both a neural network classifier trained on real H&E images and tested on virtual H&E images, and a user study with neuropathologists. Given its simple and low-cost embodiment and ability to provide real-time feedback
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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.