TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clust...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ruoqian Lin, Rui Zhang, Chunyang Wang, Xiao-Qing Yang, Huolin L. Xin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6aa85eb9566f44c7a8408daa170e0fd7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6aa85eb9566f44c7a8408daa170e0fd7
record_format	dspace
spelling	oai:doaj.org-article:6aa85eb9566f44c7a8408daa170e0fd72021-12-02T15:53:00ZTEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images10.1038/s41598-021-84499-w2045-2322https://doaj.org/article/6aa85eb9566f44c7a8408daa170e0fd72021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84499-whttps://doaj.org/toc/2045-2322Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.Ruoqian LinRui ZhangChunyang WangXiao-Qing YangHuolin L. XinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ruoqian Lin Rui Zhang Chunyang Wang Xiao-Qing Yang Huolin L. Xin TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
description	Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.
format	article
author	Ruoqian Lin Rui Zhang Chunyang Wang Xiao-Qing Yang Huolin L. Xin
author_facet	Ruoqian Lin Rui Zhang Chunyang Wang Xiao-Qing Yang Huolin L. Xin
author_sort	Ruoqian Lin
title	TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
title_short	TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
title_full	TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
title_fullStr	TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
title_full_unstemmed	TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
title_sort	temimagenet training library and atomsegnet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6aa85eb9566f44c7a8408daa170e0fd7
work_keys_str_mv	AT ruoqianlin temimagenettraininglibraryandatomsegnetdeeplearningmodelsforhighprecisionatomsegmentationlocalizationdenoisinganddeblurringofatomicresolutionimages AT ruizhang temimagenettraininglibraryandatomsegnetdeeplearningmodelsforhighprecisionatomsegmentationlocalizationdenoisinganddeblurringofatomicresolutionimages AT chunyangwang temimagenettraininglibraryandatomsegnetdeeplearningmodelsforhighprecisionatomsegmentationlocalizationdenoisinganddeblurringofatomicresolutionimages AT xiaoqingyang temimagenettraininglibraryandatomsegnetdeeplearningmodelsforhighprecisionatomsegmentationlocalizationdenoisinganddeblurringofatomicresolutionimages AT huolinlxin temimagenettraininglibraryandatomsegnetdeeplearningmodelsforhighprecisionatomsegmentationlocalizationdenoisinganddeblurringofatomicresolutionimages
_version_	1718385514024796160

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Ejemplares similares