This paper presents a deep learning-based approach to automate particle size analysis in the microscopy images of catalyst layers for polymer electrolyte fuel cells.
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.
The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often \textit{ad hoc}, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. Our approach employs training an instance segmentation model, called StarDist [Schmidt et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham], which resolves the main challenge in the pixel-wise localization of nanoparticles in TEM images: the overlapping particles. The segmentation maps outperform models reported in the literature, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.
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