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

ACS Nanoscience Au, vol. 3, pp. 398–407

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-, to rod-shaped 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 corresponding Aachen Chair of Theory and Computation of Energy Materials Faculty of Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

   Chair of Theory and Computation of Energy Materials, Faculty of Georesources and Materials Engineering, RWTH Aachen University, 52062 Aachen, Germany

   Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

2. Jenia Jitsev

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

   Jülich Supercomputing Center, Forschungszentrum Jülich, 52425 Jülich, Germany

3. Michael Hermann Eikerling Aachen Chair of Theory and Computation of Energy Materials Faculty of Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

   Chair of Theory and Computation of Energy Materials, Faculty of Georesources and Materials Engineering, RWTH Aachen University, 52062 Aachen, Germany

   Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

4. Kourosh Malek

   Forschungszentrum Jülich

   Affiliation as printed

   Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

   Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

5. Mohammad J. Eslamibidgoli corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Centre for Advanced Simulation and Analytics (CASA), Simulation and Data Science Lab for Energy Materials (SDL-EM), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

   Theory and Computation of Energy Materials (IEK-13), Institute of Energy and Climate Research, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany