Live-cell imaging powered by computation
Open accessAbstract
The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.
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Live-cell imaging plays a crucial role in biological experiments, offering insights into cellular activities.
“Beyond clinical imaging, live-cell imaging plays a crucial role in biological experiments, offering insights into cellular activities [37].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [37]
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Noise2Void - Learning Denoising from Single Noisy ImagesPreprint
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Enabling reactive microscopy with MicroMator
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Self-citationMultiscale fluorescence imaging of living samples
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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
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“Different image segmentation techniques are available and factors like image complexity, desired level of detail, and computational resources should be taken into consideration when selecting the best approach ( e.g., thresholding, edge detection, region growing) (Shroff et al. 2024).”
Volumetric localization microscopy with deep learning
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Self-citationCascaded Micro‐Ring Resonators for Low‐Crosstalk High‐Density Photonic Convolutional Computing
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Micro𝕊plit: Semantic Unmixing of Fluorescent Microscopy DataPreprint
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Self-citationImage quality metrics fail to accurately represent biological information in fluorescence microscopyPreprint
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Self-citationReal-time, high-throughput super-resolution microscopy via panoramic integration
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Single-cell phenomics through integrated imaging and molecular profilingPreprint
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Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactionsPreprint
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- Authors
- Shroff, H., Testa, I., Jug, F., & Manley, S.
- Affiliations
- Howard Hughes Medical Institute (US)
- Science for Life Laboratory (SE)
- Human Technopole (IT)
- École Polytechnique Fédérale de Lausanne (CH)
- Corresponding author
- Manley, Suliana
- Venue
- Nature Reviews Molecular Cell Biology
- Published
- 2024-02-20
- Volume
- 25
- Issue
- 6
- Pages
- 443-463
- ISSN
- 1471-0072 (ISSN-L)
- 1471-0080
- OpenAlex
- W4391971327
- Cited by
- 75
- References
- 136
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 13
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Nature Reviews Molecular Cell Biology (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Engineering › Media Technology › Image Processing Techniques and Applications
- Keywords
- live-cell imaging, computational microscopy, machine learning, artificial intelligence, fluorescence microscopy, signal-to-noise ratio, spatial resolution, temporal resolution, cell viability
- MeSH terms
- Animals, Artificial Intelligence, Cell Survival, Humans, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Signal-To-Noise Ratio
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