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

ChemRxiv

Abstract

This work presents the development and implementation of a deep learning-based workflow for autonomous image analysis in nanoscience. 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 training of high-performance models for microscopy image analysis based on convolutional neural networks. We demonstrate how the expanded training set can significantly improve the performance of the classification and instance segmentation models for a variety of nanoparticle shapes, ranging from spherical-, cubic-, rod-shaped to amorphous nanoparticles. Finally, the trained models were deployed in a cloud-based analytics platform for the autonomous particle analysis of microscopy images.

Authors 5

1. André Colliard-Granero Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich

   RWTH Aachen University

2. Jenia Jitsev

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich

3. Michael Hermann Eikerling Aachen

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich

   RWTH Aachen University

4. Kourosh Malek

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich

5. Mohammad J. Eslamibidgoli

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich