ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy (SMLM)

This repository holds the source accompanying our IEEE Transactions in Medical Imaging 2020 paper.

ERGO detects and predicts the density in SMLM anisotropic acquisition. High density (green proximate emitters) can cause inaccurate reconstruction (red-framed blue) of the assumed emission location.

Project organization

ERGO is a 2-stage software pipeline to identify and predict the density of emissions in single molecule localization microscopy.

This project has 2 stages:

• Localization : ERGO.jl
• Counting/Density estimation : ERGO.py

All files in this repository are licensed under Affero GPL v 3, copyright 2018-2021 Ben Cardoen.

The software was developed in a multidisciplinary collaboration between the labs of Prof. Ghassan Hamarneh, Prof. Ivan Robert Nabi, and Prof. Keng C. Chou. This project could not have been realized without my other co-authors: Hanene Ben Yedder, and Anmol Sharma.

Manuscript :

http://www.cs.sfu.ca/~hamarneh/ecopy/tmi2020.pdf

If you use, or find this work useful, please cite the below paper:

@article{cardoen2019ergo,
  title={Ergo: efficient recurrent graph optimized emitter density estimation in single molecule localization microscopy},
  author={Cardoen, Ben and Yedder, Hanene Ben and Sharma, Anmol and Chou, Keng C and Nabi, Ivan Robert and Hamarneh, Ghassan},
  journal={IEEE transactions on medical imaging},
  volume={39},
  number={6},
  pages={1942--1956},
  year={2019},
  publisher={IEEE}
}