DECODE Manual#

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.

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.

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.