UTILE-Gen (UTILE-Gen)

Authors: Andre Colliard Granero, Jenia Jitsev, Kourosh Malek, Michael Eikerling, Mohammad Eslamibidgoli

Keywords: deeplearning, machinelearning, synthetic, data, generator, domainrandomization, label, mask, image, electronmiscroscopy, electronmicroscope,TEM,SEM

A versatile, agnostic, and configurable tool was developed to generate instance-segmented imaging datasets of nanoparticles. The synthetic generator tool employs domain randomization to expand the image/mask pairs dataset for training supervised deep learning models. The approach eliminates tedious manual annotation and allows the training of high-performance models for microscopy image analysis based on convolutional neural networks.


Publications

UTILE-Gen: Automated Image Analysis in Nanoscience Using Synthetic Dataset Generator and Deep Learning

Colliard-Granero A, Jitsev J, Eikerling M, Malek K, Eslamibidgoli M - ACS Nanoscience Au - 2023



UTILE-Gen Image
License
CC-BY 4.0

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