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 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.

Joint Detection of Tap and CEA Based on Deep Learning Medical Image Segmentation: Risk Prediction of Thyroid Cancer

“…Based on this, this paper proposes a model for the segmentation of learning score sets. This model adds a global directional energy element to the LBF model, which exceeds the sensitivity of the LBF model in the original contour and improves the segmentation of the fuzzy image boundary model [ 15 , 16 ].…”

Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise

“…Convolutional neural networks (CNNs) achieve state-of-theart denoising performance on natural images (Zhang et al, 2017;Tian et al, 2019) and are an emerging tool in various fields of scientific imaging, for example, in fluorescence light microscopy (Belthangady & Royer, 2019;Zhang et al, 2019) and in medical diagnostics (Yang et al, 2017;Jifara et al, 2019). In electron microscopy, deep CNNs are rapidly being developed for denoising in a variety of applications, including structural biology (Buchholz et al, 2019;Bepler et al, 2020), semiconductor metrology (Chaudhary et al, 2019;Giannatou et al, 2019), and drift correction (Vasudevan & Jesse, 2019), among others (Ede & Beanland, 2019;Lee et al, 2020;Wang et al, 2020;Lin et al, 2021;Spurgeon et al, 2021), as highlighted in a recent review (Ede, 2020). CNNs trained for segmentation have also been used to locate the position of atomic columns (Lin et al, 2021) as well as to estimate their occupancy (Madsen et al, 2018) in relatively high SNR (S)TEM images (i.e., SNR = ∼10).…”

Atomically Dispersed CoN3C1-TeN1C3 Diatomic Sites Anchored in N-doped Carbon as Efficient Bifunctional Catalyst for Synergistic Electrocatalytic Hydrogen Evolution and Oxygen Reduction

“…S9d). In order to further confirm the existence of Co and Te atoms, AtomSegNet App was used to conduct super-resolution processing and tracking of atomic features in AC-HAADF-STEM images 19,20 . The bright spots of uniform size can be observed in the atom detect image of Te SASs/N-C (Fig.…”