Deep learning to classify nanostructured materials with heterogeneous composition from transmission electron microscopy images

2020 ◽  
Author(s):  
Carlos Cabrera ◽  
Patricia Juárez ◽  
David Cervantes ◽  
Franklin Muñoz ◽  
Gustavo Hirata ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Deep Learning ◽  
Nanostructured Materials ◽  
Transmission Electron ◽  
Microscopy Images

scholarly journals A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images

2018 ◽  
Vol 1 (8) ◽  
pp. 1800037 ◽  
Author(s):  
Jacob Madsen ◽  
Pei Liu ◽  
Jens Kling ◽  
Jakob Birkedal Wagner ◽  
Thomas Willum Hansen ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Deep Learning ◽  
Atomic Resolution ◽  
Learning Approach ◽  
Local Structures ◽  
Transmission Electron ◽  
Microscopy Images

scholarly journals Deep Learning–Based Workflow for Analyzing Helium Bubbles in Transmission Electron Microscopy Images

2021 ◽  
Vol 27 (S1) ◽  
pp. 2132-2133
Author(s):  
Chun Yin Wong ◽  
Xing Wang ◽  
Zhe Fan ◽  
Karren More ◽  
Sergei Kalinin ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Deep Learning ◽  
Helium Bubbles ◽  
Transmission Electron ◽  
Microscopy Images

scholarly journals Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Estibaliz Gómez-de-Mariscal ◽  
Martin Maška ◽  
Anna Kotrbová ◽  
Vendula Pospíchalová ◽  
Pavel Matula ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Deep Learning ◽  
Extracellular Vesicles ◽  
Physiological Condition ◽  
State Of The Art ◽  
Automatic Quantification ◽  
Transmission Electron ◽  
Complex Procedure ◽  
Microscopy Images

Abstract Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30–200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.

scholarly journals A deep learning approach for determining the chiral indices of carbon nanotubes from high-resolution transmission electron microscopy images

Carbon ◽  
2020 ◽  
Vol 169 ◽  
pp. 465-474
Author(s):  
Georg Daniel Förster ◽  
Alice Castan ◽  
Annick Loiseau ◽  
Jaysen Nelayah ◽  
Damien Alloyeau ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Carbon Nanotubes ◽  
Transmission Electron Microscopy ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Approach ◽  
Transmission Electron ◽  
Microscopy Images
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