This is the documentation of the DECODE Deep Learning for Superresolution Localization Microscopy.
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.
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.
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.
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.