introduction
Fluorescence microscopy-based image restoration has received a lot of attention in the field of life sciences and has made significant progress, thanks to deep learning technology. However, most of the current task-specific methods have limited commonality for different fluorescence microscopy-based image restoration problems.
On April 12, 2024, Yan Bo's team from Fudan University published a research paper entitled "Pretraining a foundation model for generalizable fluorescence microscopy-based image restoration" online in Nature Methods, which proposed a fluorescence microscopy-based universal image restoration (UniFMIR) model to solve different restoration problems and show that UniFMIR has higher image restoration accuracy, better generalization and more versatility.
The demonstration of five tasks and 14 datasets, covering a wide range of microscopic imaging modalities and biological samples, demonstrates that pre-trained UniFMIR can efficiently transfer knowledge to specific situations through fine-tuning, reveal clear nanoscale biomolecular structures, and facilitate high-quality imaging. This work has the potential to excite and trigger new research highlights for fluorescence microscopy-based image restoration.
Fluorescence microscopy image restoration (FMIR), which aims to provide images with a high signal-to-noise ratio from images with a low signal-to-noise ratio, has received great attention from the research community as it helps to reveal important nanoscale imaging information for accurate observation and scientific analysis of biological structures and processes. At present, benefiting from the rapid development of deep learning, there is a large influx of contributions in this area in the literature. Many deep learning-based fluorescence microscopy-based image restoration efforts have computationally pushed the physical limits of fluorescence microscopy and made significant improvements to classical deconvolution algorithms. Despite significant progress, these deep learning-based fluorescence microscopy-based image restoration methods are still subject to some weaknesses that limit the further development of biological processes. First, mainstream models address specific image restoration problems based on fluorescence microscopy, such as denoising, super-resolution (SR), and anisotropic reconstruction, by training specific depth models (e.g., U-Net heuristic models and RCAN-inspired models) with finite parameters (no more than millions) from scratch on a specific dataset. In addition, these models have poor generalization ability, and significant performance degradation can be observed when faced with large domain gaps between different datasets and different fluorescence microscopy-based image restoration problems. Achieving promising results in different imaging modalities, biological samples, and image restoration tasks requires training multiple specific models. Finally, the common data dependency problem in the field of deep learning also affects most fluorescence microscopy-based image restoration models, and their performance is highly dependent on the quality and quantity of training data due to the data-driven nature of deep learning-based methods. Therefore, the practical difficulty of obtaining low-quality and high-quality training image pairs in experiments complicates the practical application of deep learning-based fluorescence microscopy image restoration methods.
UniFMIR在体积重建中的应用（Credit: Nature Methods）
While task-specific or mode-specific depth models are still the dominant deep learning methods for fluorescence microscopy-based image restoration. While state-of-the-art (SOTA) performance can now be achieved with a single model, the base model has the advantage of versatility. Rather than training a new model from scratch for each task, the above approach proves that the base model can democratize the basic knowledge learned in the general dataset during the pre-training phase and transfer this knowledge to numerous tasks through fine-tuning. The great progress of pre-trained large-scale models has brought new impetus to the development of fluorescence microscopy-based image restoration methods. In conclusion, UniFMIR provides a versatile solution for image enhancement in fluorescence microscopy, and the pre-trained UniFMIR can be applied to different tasks, imaging modalities, and biological structures with simple parameter fine-tuning, demonstrating the tremendous impact of the basic model approach on bioimaging research. In the future, the image reconstruction capability of UniFMIR can be continuously enhanced by further expanding the data volume and richness of the training data.
Original link https://www.nature.com/articles/s41592-024-02244-3
Editor-in-charge|Explore Jun
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文章来源|“ iNature"
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