Deep learning-based super-resolution fluorescence microscopy on small datasets

TL;DR

This paper introduces a convolutional neural network approach capable of producing super-resolution fluorescence microscopy images from small datasets, overcoming the limitations of traditional deep learning methods that require large training data.

Contribution

A novel CNN-based method that successfully trains on small datasets for super-resolution microscopy, applicable to various biomedical imaging modalities.

Findings

Achieved super-resolution imaging with only 750 images from 15 FOVs.

Traditional models failed with small datasets, but the new approach succeeded.

Potential application to MRI and X-ray imaging where data is limited.

Abstract

Fluorescence microscopy has enabled a dramatic development in modern biology by visualizing biological organisms with micrometer scale resolution. However, due to the diffraction limit, sub-micron/nanometer features are difficult to resolve. While various super-resolution techniques are developed to achieve nanometer-scale resolution, they often either require expensive optical setup or specialized fluorophores. In recent years, deep learning has shown the potentials to reduce the technical barrier and obtain super-resolution from diffraction-limited images. For accurate results, conventional deep learning techniques require thousands of images as a training dataset. Obtaining large datasets from biological samples is not often feasible due to the photobleaching of fluorophores, phototoxicity, and dynamic processes occurring within the organism. Therefore, achieving deep learning-based super-resolution using small datasets is challenging. We address this limitation with a new convolutional neural network-based approach that is successfully trained with small datasets and achieves super-resolution images. We captured 750 images in total from 15 different field-of-views as the training dataset to demonstrate the technique. In each FOV, a single target image is generated using the super-resolution radial fluctuation method. As expected, this small dataset failed to produce a usable model using traditional super-resolution architecture. However, using the new approach, a network can be trained to achieve super-resolution images from this small dataset. This deep learning model can be applied to other biomedical imaging modalities such as MRI and X-ray imaging, where obtaining large training datasets is challenging.