ConfocalGN : a minimalistic confocal image simulator

TL;DR

ConfocalGN is a user-friendly software tool that generates synthetic confocal microscopy images from ground truth data, enabling validation of image analysis pipelines and training machine learning models with realistic noise characteristics.

Contribution

The paper introduces ConfocalGN, a minimalistic, Matlab-based confocal image simulator that can analyze real data for noise parameters and generate realistic synthetic images.

Findings

Successfully generates synthetic images with realistic noise

Can analyze real confocal stacks to derive noise parameters

Useful for validating image analysis and training machine learning

Abstract

SUMMARY : We developed a user-friendly software to generate synthetic confocal microscopy images from a ground truth specified as a 3D bitmap with pixels of arbitrary size. The software can analyze a real confocal stack to derivate noise parameters and will use them directly to generate new images with similar noise characteristics. Such synthetic images can then be used to assert the quality and robustness of an image analysis pipeline, as well as be used to train machine-learning image analysis procedures. We illustrate the approach with closed curves corresponding to the microtubule ring present in blood platelet. AVAILABILITY AND IMPLEMENTATION: ConfocalGN is written in Matlab but does not require any toolbox. The source code is distributed under the GPL 3.0 licence on https://github.com/SergeDmi/ConfocalGN.