ArticleNature methods2024
Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Time-lapse RESOLFT nanoscopy empowered by deep learning.Nature communications · 2026Article
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- A foundational architecture for AI agents in healthcare.Cell reports. Medicine · 2025Review
- Taking a closer look at matrix vesicle biogenesis.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.Molecular biology of the cell · 2025Article
- Article
- From Multi-Omics to Visualization and Beyond: Bridging Micro and Macro Insights in CAR-T Cell Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A lightweight large receptive field network LrfSR for image super-resolution.Scientific reports · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- Content-aware motion correction for multi-shot imaging.Nature methods · 2024Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- PostFocus: automated selective post-acquisition high-throughput focus restoration using diffusion model for label-free time-lapse microscopy.Bioinformatics (Oxford, England) · 2024Article
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
- Live-cell imaging in the deep learning era.Current opinion in cell biology · 2023Review
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Authors and funding
12 authors.
Funding
Abstract
The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI's performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.
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Registered trials
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