Intelligent Detection of the Nanomaterials Spatial Structure with Synthetic Electron Microscopy Images

Polyanichenko, Dmitry S.; Чернов, А. В.; Kartashov, Oleg O.; Alexandrov, A. A.; Butova, Vera V.; Butakova, Maria A.

doi:10.1109/scm55405.2022.9794885

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Likewise, Polyanichenko et al 25 generated synthetic data based on 3D models of metal−organic frameworks and trained a model to detect and analyze such structures in real time. Due to the extreme complexity and diversity of nanoparticle systems in terms of shape, size and spatial distribution, various textures, or occlusions, it is essential to develop a versatile, system-agnostic, and configurable program that can generate high-quality annotated datasets for DL model training.…”

Section: ■ Introductionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: ■ Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Likewise, Polyanichenko et al 25 Virtual Minds (ViMi) Labs, 26 which offers an interface for high-throughput image analysis for functional energy materials. along with the generated synthetic image using the tool developed in this work.…”

Section: Introductionmentioning

confidence: 99%

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

Colliard-Granero

Jitsev

Eikerling

et al. 2023

Preprint

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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.

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Intelligent Analysis of Optical Microscopy Images for Microfluidic Synthesis Results

Alexandrov,

Kartashov,

Polyanichenko

et al. 2023

2023 XXVI International Conference on Soft Computing and Measurements (SCM)

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Intelligent Detection of the Nanomaterials Spatial Structure with Synthetic Electron Microscopy Images

Cited by 4 publications

References 18 publications

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

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

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

Intelligent Analysis of Optical Microscopy Images for Microfluidic Synthesis Results

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