.. image:: https://rieslab.de/assets/img/decode_microtubules.png
DECODE Manual
=============
This is the documentation of the DECODE Deep Learning for Superresolution Localization Microscopy.
..
_THIS IS A COPY OF THE README.MD PART. DO NOT EDIT THIS DIRECTLY. EDIT README AND COPY
DECODE is a Python and `Pytorch `__ 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 `__
software benchmark competition, it
`outperformed `__
all other fitters on 12 out of 12 data-sets when comparing 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 predict detection and localization uncertainties,
which can be used to generate superior super-resolution reconstructions.
Get Started
###########
To try out DECODE we recommend to first have a look at the Google Colab notebooks.
DECODE on Google Colab
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Our notebooks below comprise training a model, fitting experimental data and exporting the fitted localizations.
* `Training a DECODE model `_
* `Fitting high-density data `_
DECODE on your machine
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The installation is described in detail here `installation instructions. `__
Once you have installed DECODE on your local machine, please follow our
`Tutorial. `__
Video tutorial
###############
As part of the virtual `I2K 2020 `__ 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.*
.. raw:: html
Contents
########
.. toctree::
:maxdepth: 0
installation
tutorial
data
logging
faq
.. toctree::
:maxdepth: 1
:caption: DECODE API
decode