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https://github.com/TuragaLab/DECODE

This is the official implementation of our publication "Deep learning enables fast and dense single-molecule localization with high accuracy" (Nature Methods)
https://github.com/TuragaLab/DECODE

deep-learning gpu high-density localization-microscopy microscopy pytorch smlm

Last synced: 29 days ago
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This is the official implementation of our publication "Deep learning enables fast and dense single-molecule localization with high accuracy" (Nature Methods)

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README 

        
# DECODE

[![Gateway Test](https://github.com/TuragaLab/DECODE/actions/workflows/test_gateway.yml/badge.svg)](https://github.com/TuragaLab/DECODE/actions/workflows/test_gateway.yml)

[![Unit Tests](https://github.com/TuragaLab/DECODE/actions/workflows/unit_tests.yml/badge.svg)](https://github.com/TuragaLab/DECODE/actions/workflows/unit_tests.yml)

[![Docs](https://readthedocs.org/projects/decode/badge/?version=master)](https://decode.readthedocs.io/en/master/?badge=master)



DECODE is a Python and [Pytorch](http://pytorch.org/) based deep learning tool for single molecule 

localization microscopy (SMLM). It has high accuracy for a large range of imaging modalities and 

conditions. 

On the public [SMLM 2016](http://bigwww.epfl.ch/smlm/challenge2016/) software benchmark competition,

it [outperformed](http://bigwww.epfl.ch/smlm/challenge2016/leaderboard.html) all other fitters on 

12 out of 12 data-sets on both detection accuracy and localization error, often by a 

substantial margin. DECODE enables live-cell SMLM data with reduced light exposure in just 3 

seconds and to image microtubules at ultra-high labeling density.



DECODE works by training a DEep COntext DEpendent (DECODE) neural network to detect and localize 

emitters at sub-pixel resolution. Notably, DECODE also predicts detection and localization 

uncertainties, which can be used to generate superior super-resolution reconstructions.



## Getting started



The easiest way to try out the algorithm is to have a look at the Google Colab Notebooks. 

We provide them for training our algorithm and fitting experimental data. For installation instructions and further 

information please **refer to our** [**docs**](https://decode.readthedocs.io).

You can find these here:



- [Documentation](https://decode.readthedocs.io)

- DECODE Training (**NEW: v0.10**) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1uQ7w1zaqpy9EIjUdaLyte99FJIhJ6N8E?usp=sharing)

- DECODE Fitting (**NEW: v0.10**) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HAvJUL29vVuCHMZHMbU9jxd4fbLIPdhZ?usp=sharing)



## Local Installation



Details about the installation can be found in the [documentation](https://decode.readthedocs.io).



### DECODE cloud

Please reach out to Lucas ([email protected]) if you want to use DECODE, but you do not have the right hardware, or

want to use it at a larger scale.



## Video Tutorial

As part of the virtual [I2K 2020](https://www.janelia.org/you-janelia/conferences/from-images-to-knowledge-with-imagej-friends) conference we organized a workshop on DECODE.

Please find the video below.

*DECODE is being actively developed, therefore the exact commands might differ from those shown in the video.*



[![DECODE Video Tutorial](https://img.youtube.com/vi/zoWsj3FCUJs/0.jpg)](https://www.youtube.com/watch?v=zoWsj3FCUJs)



## Paper

This is the *official* implementation of the [publication](https://rdcu.be/cw7uV).



Artur Speiser*, Lucas-Raphael Müller*, Philipp Hoess, Ulf Matti, Christopher J. Obara, Wesley R. Legant, Anna Kreshuk, Jakob H. Macke†, Jonas Ries†, and Srinivas C. Turaga†, **Deep learning enables fast and dense single-molecule localization with high accuracy.** Nat Methods (2021). https://doi.org/10.1038/s41592-021-01236-x



### Data availability

The data referred to in our paper can be accessed at the following locations:

- Fig 3: Can be downloaded from the SMLM 2016 challenge [website](http://bigwww.epfl.ch/smlm/challenge2016/)

- Fig 4: [here](https://oc.embl.de/index.php/s/SFM6Pc8RetX09pJ)

- Fig 5: By request from the authors Wesley R Legant, Lin Shao, Jonathan B Grimm, Timothy A Brown, Daniel E Milkie, Brian B Avants, Luke D Lavis & Eric Betzig, [**High-density three-dimensional localization microscopy across large volumes**](https://www.nature.com/articles/nmeth.3797), _Nature Methods_, *13*, pages 359–365 (2016).



## Contributors

If you want to get in touch, the best way to get your questions answered is our [**GitHub discussions page**](https://github.com/TuragaLab/DECODE/discussions)

- Artur Speiser ([@aspeiser](https://github.com/ASpeiser), [email protected])

- Lucas-Raphael Müller ([@haydnspass](https://github.com/Haydnspass), [email protected])



## Support



Jakob H. Macke and Artur Speiser were supported by the German Research Foundation (DFG) through Germany’s Excellence Strategy (EXC-Number 2064/1, project no. 390727645) and the German Federal Ministry of Education and Research (BMBF, project no. [ADIMEM](https://fit.uni-tuebingen.de/Project/Details?id=9199), FKZ 01IS18052). 

Srinivas C. Turaga is supported by the Howard Hughes Medical Institute. Jonas Ries, Lucas-Raphael Mueller and Philipp Hoess were supported by the European Molecular Biology Laboratory, the European Research Council (grant no. CoG-724489 to Jonas Ries) and the National Institutes of Health Common Fund 4D Nucleome Program (grant no. U01 EB021223 to Jonas Ries). 



### Join us

We offer several open positions. Please take a look at the [pdf](https://www.embl.de/download/ries/other/Simalesam_ad.pdf) on how to apply.



### Acknowledgements

- Don Olbris ([@olbris](https://github.com/olbris), [email protected]) for help with python packaging.