ArticleNature photonics2023
Artificial confocal microscopy for deep label-free imaging.
Article in Nature photonics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- Systematic modeling of phenotypic drug response profiles in patient-derived organoids.bioRxiv : the preprint server for biology · 2026Article
- Scattering-enabled epi-quantitative phase imaging reveals subcellular detail in organoids and deep mouse brains.bioRxiv : the preprint server for biology · 2026Article
- Neural phase microscopy with metasurface optics for real-time and nanoscale quantitative phase imaging.Nature communications · 2026Article
- Machine learning-powered single-molecule cancer diagnosis using DNA origami tags.Science advances · 2026Article
- Artificial Neural Networks for Bioimage Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.Journal of biomedical optics · 2025Article
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Drug-Induced Reversible Lysosomal Changes Tracked in Live Cells by Holo-Tomographic Flow Cytometry.ACS nano · 2025Article
- A diffusion model for universal medical image enhancement.Communications medicine · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Optical sectioning methods in three-dimensional bioimaging.Light, science & applications · 2025Review
- Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.Nature communications · 2024Article
- Quantitative phase imaging endoscopy with a metalens.Light, science & applications · 2024Article
- Article
- DMD and microlens array as a switchable module for illumination angle scanning in optical diffraction tomography.Biomedical optics express · 2024Article
- Ultra-low-cost and high-fidelity NIR-II confocal laser scanning microscope with Bessel beam excitation and SiPM detection.Biomedical optics express · 2024Article
- Quantitative phase imaging based on holography: trends and new perspectives.Light, science & applications · 2024Review
- CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.Journal of biomedical optics · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Automated Cell Lineage Reconstruction using Label-Free 4D Microscopy.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Widefield microscopy of optically thick specimens typically features reduced contrast due to "spatial crosstalk", in which the signal at each point in the field of view is the result of a superposition from neighbouring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, but comes at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. We equipped a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical path-length maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered, and the data acquisition is automated. The ACM images present significantly stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. In summary, ACM can provide quantitative, dynamic data, nondestructively from thick samples, while chemical specificity is recovered computationally.
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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.