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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: about 1 month 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)
- Host: GitHub
- URL: https://github.com/turagalab/decode
- Owner: TuragaLab
- License: gpl-3.0
- Created: 2020-08-06T23:13:31.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-06-22T11:37:19.000Z (over 1 year ago)
- Last Synced: 2024-08-04T00:12:39.032Z (3 months ago)
- Topics: deep-learning, gpu, high-density, localization-microscopy, microscopy, pytorch, smlm
- Language: Python
- Homepage:
- Size: 46.2 MB
- Stars: 88
- Watchers: 7
- Forks: 26
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# DECODE
[](https://github.com/TuragaLab/DECODE/actions/workflows/test_gateway.yml)
[](https://github.com/TuragaLab/DECODE/actions/workflows/unit_tests.yml)
[](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**) [](https://colab.research.google.com/drive/1uQ7w1zaqpy9EIjUdaLyte99FJIhJ6N8E?usp=sharing)
- DECODE Fitting (**NEW: v0.10**) [](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.*
[](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.