X-Microscopy

Deep learning enables STORM-like superresolution image reconstruction from conventional microscopy

Overview

This project is to produce the results of our paper: Deep learning enables STORM-like superresolution image reconstruction from conventional microscopy. We provide two training methods with fixed input size and flexible input size, as well as flexible input size test. The following is the instructions for using it.

System Requirements

Hardware requirements

This code requires a standard computer with enough RAM to support the in-memory operations and the GeForce GTX 1080 GPU (The NVIDIA Inc.) to support GPU computing.

Software requirements

OS Requirements

This package is supported for Linux. The package has been tested on the following systems:

• Linux: Ubuntu 16.04

Python Dependencies

This package mainly depends on the Python-3.6.4 scientific stack.

numpy
scipy
scikit-learn
pandas
tensorflow-1.13.1

GPU Dependencies

This package mainly depends on CUDA 10.0 and cudnn 7.

Installation Guide:

Install from Github

git clone https://github.com/kanshichao/X-Microscopy
cd X-Microscopy

Setting up the development environment

• The folder of UR-Net-8 contains the MU-SRM reconstruction code, and change to this folder to perform wf->MU-SRM.
• The folder of X-Net contains the F-SRM reconstruction code, and change to this folder to perform wf+MU-SRM->F-SRM.

Instructions

When you use this code, please change the corresponding folder in the code to yours, i.e., the data path in the train, evaluate, and test functions of the train.py, and in the main function of the main.py.

The parameter of --phase is to alternative the state of training or test, set as train for training and set as test for test.

• For training:

python main.py --phase train

• For test:

python main.py --phase test

The parameter of --same_input_size is to alternative fixed input size or flexible input size during the training stage. If you want to run the code with fixed input size during the training stage, you shold set the value of --same_input_size as True, otherwise, set the value of --same_input_size as False.

• For training or fine-tuning with fixed input size:

python main.py --phase train --same_input_size True

• For training or fine-tuning with flexible input size:

python main.py --phase train --same_input_size False

The script of evaluate.py is used to evaluate the performances of SRM reconstruction, which is based on the realized verison of python. When you use it, please change the corresponding folder to yours.

Pretrained models

We provide the trained models to reproduce the results that presented in our paper.

• Google Drive

For detailed technical details, please see our paper and the released code.

Citation

If you use this method or this code in your research, please cite as:

@inproceedings{XuleiKanshichao-2022,
title={Deep learning enables STORM-like superresolution image reconstruction from conventional microscopy},
author={Lei Xu, Shichao Kan, Xiying Yu, Yuxia Fu, Yiqiang Peng, Yanhui Liang, Yigang Cen, Changjun Zhu, Wei Jiang},
booktitle={},
pages={},
year={2023}
}

Acknowledgments

This code is written based on the tensorflow framework of pix2pix.

License

This code is released for academic research / non-commercial use only. This project is covered under the MIT License.