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

arXiv: https://arxiv.org/abs/2103.04989 · HTML: https://mlsanity.com/paper/2103.04989/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification](https://mlsanity.com/paper/1801.06879/) (2018)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [GANs for Biological Image Synthesis](https://mlsanity.com/paper/1708.04692/) (2017)
- [Machine learning for faster and smarter fluorescence lifetime imaging microscopy](https://mlsanity.com/paper/2008.02320/) (2020)
- [Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs](https://mlsanity.com/paper/2002.12164/) (2020)
- [Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks](https://mlsanity.com/paper/2006.13801/) (2020)

## Built on by (most cited first, 1 of 1)

- [Low dosage 3D volume fluorescence microscopy imaging using compressive sensing](https://mlsanity.com/paper/2201.00820/) (2022)

## Abstract (arXiv)

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.