ThreeDeconv.jl is a 3D deconvolution software for fluorescence microscopy written in Julia. Currently, it supports Julia v.1.6.0 but may not be going to be supported in the future releases. If interested, please feel free to make a PR. The detail of the algorithm is available in our paper and our website. While the deconvolution algorithm is the same as described in the paper, we made some improvements in our software along with the major update of Julia and related packages. Thanks to this update, this package is significantly faster than the speed reported on the paper and deconvoles a 256 x 256 x 57 image in 5 seconds! Please refer to NEWS.md to catch up the updates.
Hit the key ] in the Julia REPL to ender the Pkg REPL-mode, and run
pkg> add https://github.com/computational-imaging/ThreeDeconv.jl.gitTo run the example script, you need PyCall.jl and Conda.jl. In addition, you need scikit-image available through PyCall. You can run the following in Julia's REPL to install them.
julia> import Pkg; Pkg.add("Conda")
julia> import Conda; Conda.add_channel("conda-forge"); Conda.update(); Conda.add("scikit-image")
julia> ENV["PYTHON"] = joinpath(Conda.PYTHONDIR, "python")
julia> Pkg.add("PyCall")If you already have a Python environment that you want to use from Julia, please consult with PyCall.jl.
Since these packages are required just for image I/O, you can also use other packages if you want.
However, I personally found that scikit-image is the most convenient package to read multi-stack tiff files from Julia.
If you have a CUDA-compatible NVIDIA GPU, the example script runs on a GPU. You might want to use `PackageCompiler.jl' to create a sysimage.
Then, after downloading the dataset (described below) in the example directory, run
julia example.jl
from your terminal in the example directory. The script simulates the PSF, deconvolves the fluorescent bead and saves the deconvolved image.
The dataset used in our paper is available at Google Drive (150MB). The images are processed with darkfield and flatfield correction as described in the paper. Its raw dataset is also available at Google Drive (1GB). This dataset comes with corresponding darkfield and flatfield images.
This dataset can be used for academic and other non-commercial purposes. If you use this dataset in your research paper, please cite our paper.
The Jupyter notebook is not maintained anymore. But you may be able to easilly modify it to make it runnable by referring to example.jl
"A convex 3D deconvolution algorithm for low photon count fluorescence imaging" Scientific Reports 8, Article number: 11489 (2018) Hayato Ikoma, Michael Broxton, Takamasa Kudo, Gordon Wetzstein
@article{ikoma2018convex,
title={A convex 3D deconvolution algorithm for low photon count fluorescence imaging},
author={Ikoma, Hayato and Broxton, Michael and Kudo, Takamasa and Wetzstein, Gordon},
journal={Scientific reports},
volume={8},
number={1},
pages={11489},
year={2018},
publisher={Nature Publishing Group}
}
If you use this library for your research, please cite our paper.
If you find this package useful but feel that it lacks some functionalities for your research, feel free to create an issue in this repository or send an email @hayatoikoma. I am happy to extend this package to accomodate your need.
This package is mostly developed by @hayatoikoma, and the PSF simulator (WaveOptics.jl) is developed by @broxtronix.
WaveOptics.jl is still an unreleased Julia package for various PSF simulations.
ThreeDeconv.jl has incorporated part of WaveOptics.jl for this release.
The ThreeDeconv.jl package is licensed under the following license:
Copyright (c) 2018, Stanford University
All rights reserved.
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