The development of Single-Molecule Localization Microscopy (SMLM) has enabled the visualization of sub-cellular structures, but its temporal resolution is limited. To address this issue, a deep-convolutional neural network called LUENN has been introduced, which uses a unique architecture that rejects the isolated emitter assumption. LUENN is a Python package based on a deep CNN that utilizes the Tensorflow tool for SMLM. It is capable of achieving high accuracy for a wide range of imaging modalities and frame densities.
3D reconstruction of a live cell using LUENN
circle_LC13.mp4
The software was tested on a Linux system with Ubuntu version 7.0, and a Windows system with Windows 10 Home. Training and evaluation were run on a standard workstation equipped with 32 GB of memory, an Intel(R) Core(TM) i7 − 8700, 3.20 GHz CPU, and a NVidia GeForce Titan Xp GPU with 12 GB of video memory.
- Download this repository as a zip file (or clone it using git).
- Go to the downloaded directory and unzip it.
- The conda environment for this project is given in environment_.yml where should be substituted with your operating system. For example, to replicate the environment on a linux system use the command: conda env create -f environment_linux.yml from within the downloaded directory. This should take a couple of minutes.
- After activation of the environment using: conda activate LUENN, you're set to go!
Armin Abdehkakha, Email: [email protected]
Craig Snoeyink, Email: [email protected]