ArticleNpj imaging2024
Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images.
Article in Npj imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Rapid slide-level assessment of glomerular yield using virtual staining of unstained renal biopsy sections.Diagnostic pathology · 2026Article
- Photon absorption remote sensing (PARS): comprehensive absorption imaging enabling label-free biomolecule characterization and mapping.Scientific reports · 2026Article
- Virtual multiplex staining of the pancreatic islets across type 1 diabetes progression using a Schrödinger bridge.bioRxiv : the preprint server for biology · 2026Article
- Label-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining.NPJ digital medicine · 2026Article
- Slide-free, point-of-care imaging of core needle biopsies for rapid histologic assessment.Biomedical optics express · 2026Article
- Label-free whole slide virtual multi-staining using dual-excitation photon absorption remote sensing microscopy.Npj imaging · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Autofluorescence and deep learning in early disease detection: biological foundations, clinical applications, and future directions.Frontiers in artificial intelligence · 2026Review
- Article
- Deep Learning-Enabled Virtual Multiplexed Immunostaining of Label-Free Tissue for Vascular Invasion Assessment.BME frontiers · 2026Article
- A toolkit for generating virtual brightfield images of histological and immunohistochemical stains from multiplexed data with AI-based channel selection and image enhancement.Frontiers in bioinformatics · 2026Article
- Adaptive fluorescence lifetime imaging with per-pixel signal optimization and flexible scanning.Biomedical optics express · 2025Article
- Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy.Scientific reports · 2025Article
- Label-Free Evaluation of Lung and Heart Transplant Biopsies Using Tissue Autofluorescence-Based Virtual Staining.BME frontiers · 2025Article
- VS-FPM: Large-Format, Label-Free Virtual Histopathology Microscopy.BME frontiers · 2025Article
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9 authors.
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Abstract
Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.
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