medical-4

Electron Tomography

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Pilar Cossio, D. Rohr, F. Baruffa, M. Rampp, V. Lindenstruth, G. Hummer•Tue Sep 20 2016

This work presents highly parallelized, GPU-accelerated computer software that performs the Bayesian inference of EM (BioEM) formalism efficiently and scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time.

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AITom: Open-source AI platform for cryo-electron Tomography data analysis

Xiangrui Zeng, Min Xu•Thu Nov 07 2019

AITom is introduced, an open-source artificial intelligence platform for cryo-ET researchers that provides many public as well as in-house algorithms for performing crye-ET data analysis through both the traditional template-based or template-free approach and the deep learning approach.

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Self-Supervised Cryo-Electron Tomography Volumetric Image Restoration from Single Noisy Volume with Sparsity Constraint

Zhidong Yang, Fa Zhang, Renmin Han•Thu Sep 30 2021

Cryo-Electron Tomography (cryo-ET) is a powerful tool for 3D cellular visualization. Due to instrumental limitations, cryo-ET images and their volumetric reconstruction suffer from extremely low signal-to-noise ratio. In this paper, we propose a novel end-to-end self-supervised learning model, the Sparsity Constrained Network (SC-Net), to restore volumetric image from single noisy data in cryo-ET. The proposed method only requires a single noisy data as training input and no ground-truth is needed in the whole training procedure. A new target function is proposed to preserve both local smoothness and detailed structure. Additionally, a novel procedure for the simulation of electron tomographic photographing is designed to help the evaluation of methods. Experiments are done on three simulated data and four real-world data. The results show that our method could produce a strong enhancement for a single very noisy cryo-ET volumetric data, which is much better than the state-of-the-art Noise2Void, and with a competitive performance comparing with Noise2Noise. Code is available at https://github.com/icthrm/SC-Net.

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Statistical spatial analysis for cryo-electron tomography

A. Martínez-Sánchez, W. Baumeister, V. Lučić•Tue Jul 13 2021

PyOrg is a versatile, precise, and efficient open-source software for reliable quantitative characterization of macromolecular organization within cellular compartments imaged in situ by cryo-ET, as well as to other 3D imaging systems where real-size particles are located within regions possessing complex geometry.

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Boosting Active Learning via Improving Test Performance

Guosheng Hu, Tianyang Wang, Xingjian Li, Pengkun Yang, Xiangrui Zeng, Siyu Huang, Chengzhong Xu, Min Xu•Thu Dec 09 2021

This work theoretically proves that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in a better test performance, and proposes two schemes, namely expected-grad norm and entropy-gradnorm, which are integrated in a universal AL framework.

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Deep learning-based identification of sub-nuclear structures in FIB-SEM images

N. Gupta, Eric J. Roberts, Song Pang, C. Xu, H. Hess, Fan Wu, A. Dernburg, D. Jorgens, P. Zwart, V. Kasinath•Mon Jul 18 2022

A convolutional neural network, supervised deep learning-based approach which can identify sub-nuclear structures with 90% accuracy is presented which can be optimized for its broad application to other volumetric imaging data as well as in situ cryo-electron tomography.

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Real-Time Tilt Undersampling Optimization during Electron Tomography of Beam Sensitive Samples using Golden Ratio Scanning and RECAST3D

K. Batenburg, Timothy M. Craig, Ajinkya Kadu, S. Bals•Thu Feb 23 2023

Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction artifacts due to insufficient sampling. Therefore, it is important to determine the optimal number of projections that minimizes both beam exposure and undersampling artifacts for accurate reconstructions of beam-sensitive samples. Current methods for determining this optimal number of projections involve acquiring and post-processing multiple reconstructions with different numbers of projections, which can be time-consuming and requires multiple samples due to sample damage. To improve this process, we propose a protocol that combines golden ratio scanning and quasi-3D reconstruction to estimate the optimal number of projections in real-time during a single acquisition. This protocol was validated using simulated and realistic nanoparticles, and was successfully applied to reconstruct two beam-sensitive metal-organic framework complexes.

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A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography

Simon Wiedemann, Reinhard Heckel•Wed Nov 08 2023

A deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge, which is simpler than current state-of-the-art approaches for denoising and missing wedge reconstruction, performs competitively and produces more denoised tomograms with higher overall contrast.

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Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation

Valentin Debarnot, Vinith Kishore, Ricardo D. Righetto, Ivan Dokmani'c•Sun Mar 03 2024

ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume, relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections.

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