.. 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 """""""""""""""""""""" 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 """""""""""""""""""""" 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