/Deep-STORM

Official implementation of Deep-STORM for dense 2D localization microscopy

Primary LanguagePythonMIT LicenseMIT

Deep-STORM

This code accompanies the paper: Deep-STORM: Super resolution single molecule microscopy by deep learning

  • Update (06/2020): In case you do not have a workstation equipped with a GPU and/or want to skip the installation of the software needed for this code, you can use the Colab notebook implementation of Deep-STORM. The notebook is part of the ZeroCostDL4Mic platform featuring a self-explanatory easy-to-use graphical user interface. Besides being user-friendly, the notebook is strongly recommended as it features additional advantages like outputting a localizations list.

Contents

Overview

Deep-STORM is a single molecule localization microscopy code for training a custom fully convolutional neural network estimator and recovering super-resolution images from dense blinking movies:

System requirements

  • The software was tested on a Linux system with Ubuntu version 16.04, and a Windows system with Windows 10 Home.
  • Training and evaluation were run on a standard workstation equipped with 32 GB of memory, an Intel(R) Core(TM) i7 − 8700, 3.20 GHz CPU, and a NVidia GeForce Titan Xp GPU with 12 GB of video memory.
  • Prerequisites
    1. ImageJ >= 1.51u with ThunderSTORM plugin >= 1.3 installed.
    2. Matlab >= R2017b with image-processing toolbox.
    3. Python >= 3.5 environment with Tensorflow >= 1.4.0, and Keras >= 1.0.0 installed.

Installation instructions

  • ImageJ and ThunderSTORM

    1. Download and install ImageJ 1.51u - The software is freely available at https://imagej.nih.gov/ij/download.html
    2. Download ThunderSTORM plugin <*.jar> file from https://zitmen.github.io/thunderstorm/
    3. Place the downloaded <*.jar> file into ImageJ plugins directory
    • To verify ThunderSTORM is setup correctly, open ImageJ and navigate to the Plugins directory. The ThunderSTORM plugin should appear in the list.

  • Matlab can be downloaded at https://www.mathworks.com/products/matlab.html

  • Python environment

    1. The conda environment for this project is given in environment.yml. To replicate the environment on your system use the command: conda env create -f environment.yml from within the downloaded directory. This should take a couple of minutes.
    2. After activation of the environment using: conda activate deep-storm, you're set to go!

Usage and demo examples

  • To train a network, the user needs to perform the following steps:

    1. Simulate a tiff stack of data frames, with known ground truth positions in an accompanying csv file, using ImageJ ThunderSTORM plugin. The simulated images and positions are saved for handling in Matlab.
    2. Generate the training examples matfile in matlab using the script GenerateTrainingExamples.m.
    3. Open up the anaconda command prompt, and activate the previously created deepstorm environment by using the command: activate deepstorm.
    4. Train a convolutional neural network for the created examples using the command: python Training.py --filename <path_of_the_generated_training_examples_mfile> --weights_name <path_for_saving_the_trained_weights_hdf5_file> --meanstd_name <path_for_saving_the_normalization_factors_mfile>

  • To use the trained network on data for image reconstruction from a blinking movie, run the command: python Testing.py --datafile <path_to_tiff_stack_for_reconstruction> --weights_name <path_to_the_trained_model_weights_as_hdf5_file> --meanstd_name <path_to_the_saved_normalization_factors_as_mfile> --savename <path_for_saving_the_Superresolution_reconstruction_matfile> --upsampling_factor <desired_upsampling_factor> --debug <boolean (0/1) for saving individual predictions>

    • Note: The inputs upsampling_factor and debug are optional. By default, the upsampling_factor is set to 8, and debug is set to 0.

  • There are 2 different demo examples that demonstrate the use of this code:

    1. demo1 - Simulated Microtubules - learning a CNN for localizing simulated microtubules structures obtained from the EPFL 2013 Challenge (Fig. 4 main text). It takes approximately 2 hours to train a model from scratch on a Titan Xp. See the [pdf instructions](https://github.com/EliasNehme/Deep-STORM/blob/master/demo1 - Simulated Microtubules/demo1.pdf) inside this folder for a detailed step by step application of the software, with snapshots and intermediate outputs.
    2. demo2 - Real Microtubules - pre-trained CNN on simulations for localizing experimental microtubules (Fig. 6 main text).

Colab notebook

  • We have recently (June 2020) collaborated with the Jacquemet and the Henriques labs to incorporate Deep-STORM into the ZeroCostDL4Mic platform as a Colab notebook. Users are encouraged to work with the notebook version of the software as it allows 3 significant advantages over this implementation:

    1. The user does not need to have access to a GPU-acccelerated workstation as the computation is performed freely on Google cloud.
    2. No prior installation of Matlab/Python is required as the needed functions and packages are installed automatically in the notebook.
    3. Deep-STORM is extended to output localizations instead of directly outputting the super-resolved image. This feature is valuable for users intending to use the localizations afterwards for down stream analysis (e.g. single-particle-tracking).

Citation

If you use this code for your research, please cite our paper:

@article{nehme2018deep,
  title={Deep-STORM: super-resolution single-molecule microscopy by deep learning},
  author={Nehme, Elias and Weiss, Lucien E and Michaeli, Tomer and Shechtman, Yoav},
  journal={Optica},
  volume={5},
  number={4},
  pages={458--464},
  year={2018},
  publisher={Optical Society of America}
}

Important Disclaimer: When using the notebook implementation of Deep-STORM please also cite the ZeroCostDL4Mic paper.

License

This project is covered under the MIT License.

Contact

To report any bugs, suggest improvements, or ask questions, please contact me at "seliasne@campus.technion.ac.il"