CITATION.cff

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cff-version: 1.2.0
title: LUENN
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Armin
    family-names: Abdehkakha
    email: arminabd@buffalo.edu
    affiliation: University at Buffalo
  - given-names: Craig
    family-names: Snoeyink
    email: craigsno@buffalo.edu
    affiliation: University at Buffalo
    orcid: 'https://orcid.org/0000-0001-7215-2554'
repository-code: 'https://github.com/arminabdeh/LUENN_tf_version'
abstract: >-
  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. 
keywords:
  - Convolutional Neural Network
  - Single Molecule Localization Microscopy
  - Super-Resolution Microscopy
license: MIT