MOTIVATION: Serial section electron microscopy (ssEM) is essential for studying biological cell structures at nanometer resolution. However, supporting film folding (SFF) degradation frequently occurs during sample preparation, causing structural distortions and information loss that severely impair downstream analyses such as 3D reconstruction and neuron segmentation. RESULTS: We propose RegInpaint, a novel recovery framework that jointly addresses deformation correction and missing-information restoration caused by SFF degradation. RegInpaint formulates SFF recovery as a joint problem of 3D elastic registration and image inpainting, providing a generalizable solution for ssEM restoration. Experiments on four EM datasets show that RegInpaint consistently outperforms existing methods in image restoration quality and significantly improves neuron segmentation accuracy. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/zhangzhenbang2021/RegInpaint.git.

Series Section Electron Microscopy (ssEM) is a crucial technique for visualizing three-dimensional (3D) biological structures, which involves collecting electron microscopy images from a series of biological sections along the z-axis and reconstructing the 3D structure. 3D registration is an essential step in ssEM, designed to eliminate axial misalignment and nonlinear distortions introduced during sample sectioning. A significant challenge in 3D registration is eliminating nonlinear distortions while preserving natural deformations. In this paper, we present a new formulation of the 3D registration problem from a frequency domain perspective and propose a Gaussian filtering-based 3D registration method, which defines 3D registration as a superposition problem of high-frequency and low-frequency components. We extend the concept of a one-dimensional Gaussian filter to three-dimensional image stacks and integrate it with optical flow networks to consolidate the deformation field within the receptive field. Extensive experiments demonstrate that our method can successfully decouple nonlinear distortions and natural deformations in the frequency domain, proving superior to existing methods in rapidly and accurately eliminating nonlinear distortions and restoring biological structures, and has the potential to be extended to large datasets.

Series Section Electron Microscopy (ssEM) has emerged as a pivotal technology for deciphering nanoscale biological architectures. Three-dimensional (3D) registration is a critical step in ssEM, tasked with rectifying axial misalignments and nonlinear distortions introduced during serial sectioning. The core scientific challenge lies in achieving distortion mitigation without erasing the natural morphological deformations of biological tissues, thereby enabling faithful reconstruction of 3D ultrastructural organization. In this study, we present a paradigm-shifting optimization framework that rethinks 3D registration through the lens of manifold trajectory optimization. We propose the first continuous trajectory dynamics formulation for 3D registration and introduce a novel optimization strategy. Specifically, we introduce a dual optimization objective that inherently balances global trajectory smoothness with local structural preservation, while developing a solver that combines Gauss-Seidel iteration with Neural ODEs to systematically integrate biophysical priors with data-driven deformation compensation. A key strength of our method is its fully unsupervised training, which avoids reliance on ground truth and suits ssEM scenarios where annotations are difficult to obtain. Extensive experiments on multiple datasets spanning diverse tissue types demonstrate our method's superior performance in structural restoration accuracy and cross-tissue robustness.

MOTIVATION: Cryo-electron tomography (cryo-ET) has revolutionized our ability to observe structures from the subcellular to the atomic level in their native states. Achieving high-resolution reconstruction involves collecting tilt series at different angles and subsequently backprojecting them into 3D space or iteratively reconstructing them to build a 3D volume of the specimen. However, the intricate computational demands of tomographic reconstruction pose significant challenges, requiring extensive calculation times that hinder efficiency, especially with large and complex datasets. RESULTS: We present TiltRec, an open-source toolkit that leverages the parallel capabilities of Central Processing Units and Graphics Processing Units to enhance tomographic reconstruction. TiltRec implements six classical tomographic reconstruction algorithms, utilizing optimized parallel computation strategies and advanced memory management techniques. Performance evaluations across multiple datasets of varying sizes demonstrate that TiltRec significantly improves efficiency, reducing computational times while maintaining reconstruction resolution. SUMMARY: TiltRec effectively addresses the computational challenges associated with cryo-ET reconstruction by fully exploiting parallel acceleration. As an open-source tool, TiltRec not only facilitates extensive applications by the research community but also supports further algorithm modifications and extensions, enabling the continued development of novel algorithms. AVAILABILITY AND IMPLEMENTATION: The source code, documentation, and sample data can be downloaded at https://github.com/icthrm/TiltRec.

In cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), high-resolution three-dimensional structures of biological macromolecules are determined by iteratively aligning and averaging a large number of two-dimensional projections of molecules. Since the correlation measures are sensitive to the signal-to-noise ratio, various parameter estimation steps in SPA will be disturbed by the high-intensity noise in cryo-EM. However, denoising algorithms tend to damage high frequencies and suppress mid- and high-frequency contrast of micrographs, which exactly the precise parameter estimation relies on, therefore, limiting their application in SPA. In this study, we suggest combining a cryo-EM image processing pipeline with denoising and maximizing the signal's contribution in various parameter estimation steps. To solve the inherent flaws of denoising algorithms, we design an algorithm named MScale to correct the amplitude distortion caused by denoising and propose a new orientation determination strategy to compensate for the high-frequency loss. In the experiments on several real datasets, the denoised particles are successfully applied in the class assignment estimation and orientation determination tasks, ultimately enhancing the quality of biomacromolecule reconstruction. The case study on classification indicates that our strategy not only improves the resolution of difficult classes (up to 5 Å) but also resolves an additional class. In the case study on orientation determination, our strategy improves the resolution of the final reconstructed density map by 0.34 Å compared with conventional strategy. The code is available at https://github.com/zhanghui186/Mscale.

Cryo-electron microscopy (cryo-EM) single particle analysis (SPA) has been an indispensable technology to reconstruct three-dimensional (3D) structures of biomolecules at near-atomic resolution. Tens of thousands of particles are required to obtain high-resolution 3D reconstructions, nevertheless, it is rather challenging due to the extremely noisy microscopy images and the diversity of particles. Recently, while deep learning-based methods have been devoted into the improvement of particle feature extraction and location estimation, most of them are plagued with vulnerable feature representation, inexact supervised ground truth. Furthermore, these DL-methods usually adopt denoising and particle picking as two-stage operations in the existing pipeline, which is inadequate to achieve accurate estimation for location. In this paper, we propose a segmentation-aware synergy framework to automatically select particles in which two tightly-coupled networks are designed including a multiple output convolution subnet for denoise to jointly learn strong object representation and pixel representation simultaneously and a deep convolution subnet for particle location. Furthermore, joint learning of the two networks can effectively enhance the synergy relationship between denoising and downstream recognition, thus leading to accurate and reliable location estimations for SPA. When applied with various EMPAIR real-world datasets, our model improves the performance of particle detection and exaction, especially intersection over union metric, and this strength has important implications for the next 2D alignment, 2D classification averaging, and high-resolution 3D refinement steps in SPA.

MOTIVATION: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. RESULTS: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly learning the noise distribution of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. Our work verifies the feasibility of denoising based on mining the complex cryo-EM noise patterns directly from the noise patches. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising, especially in background noise removal, compared with the commonly used methods. Moreover, a case study on the real dataset demonstrates that NT2C can greatly alleviate the obstacles caused by the SNR to particle picking and simplify the identifying of particles. AVAILABILITYAND IMPLEMENTATION: The code is available at https://github.com/Lihongjia-ict/NoiseTransfer2Clean/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-particle cryo-electron microscopy (cryoEM) methods are powerful for solving high-resolution structures of biological macromolecules. Locating numerous particles from micrographs is essential for three-dimensional reconstruction but challenging due to the extremely low signal-to-noise ratio and various particle shapes in micrographs. In this study, we devise the TransPicker, a two-dimensional particle picking framework based on a novel end-to-end transformer-based detective method named crDETR (cryoEM DEtection TRansformer). crDETR applies an improved deformable Transformer to perform inference in parallel on particle relocation and global context, without hand-crafted components like anchors, non-maximum suppression procedure, or sliding windows. Also, it uses a combined loss function to guarantee fast convergence. It uses divide-and-conquer to overcome the limitations of the object query number. Moreover, we develop a series of optimizations, including denoising, enhancing, bad particle filtering, adding masks on carbon areas and ice contaminants to decrease the false-positive ratio and improve the accuracy of particle picking. Experimental results on various datasets demonstrate that TransPicker can select particles with more accuracy, especially in high noise compared with other methods. To our knowledge, TransPicker is the first application of the transformer technique in cryoEM particle picking.