ConfocalGN : a minimalistic confocal image simulator
This tool addresses the need for synthetic data to validate and train image analysis methods in microscopy, but it is incremental as it builds on existing simulation approaches.
The authors developed ConfocalGN, a user-friendly software for generating synthetic confocal microscopy images from 3D bitmaps, which can analyze real images to replicate noise characteristics and be used to test or train image analysis pipelines, as demonstrated with microtubule rings in blood platelets.
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.