Journal of microscopy最新文献

{"title":"Model-free machine learning-based 3D single molecule localisation microscopy","authors":"Miguel A. Boland,&nbsp;Jonathan P. E. Lightley,&nbsp;Edwin Garcia,&nbsp;Sunil Kumar,&nbsp;Chris Dunsby,&nbsp;Seth Flaxman,&nbsp;Mark A. A. Neil,&nbsp;Paul M. W. French,&nbsp;Edward A. K. Cohen","doi":"10.1111/jmi.13420","DOIUrl":"10.1111/jmi.13420","url":null,"abstract":"<p>Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, for example, to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call <i>‘easyZloc'</i>) utilising a lightweight Convolutional Neural Network that is generally applicable, including with ‘standard’ (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and an actin sample over a larger axial range are also shown.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"299 1","pages":"77-87"},"PeriodicalIF":1.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.13420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation steered scanning with spiral scanning path for AFM to correct image distortion with real-time compensation","authors":"Liansheng Zhang,&nbsp;Yongyun Liang,&nbsp;Wenbo Xia,&nbsp;Rongjun Cheng,&nbsp;Hongli Li,&nbsp;Qiangxian Huang","doi":"10.1111/jmi.13422","DOIUrl":"10.1111/jmi.13422","url":null,"abstract":"<p>In the field of atomic force microscopy (AFM), image quality is frequently compromised by distortions that impact measurement precision. These distortions are caused by a combination of factors such as the hysteresis, creep, and drift of the piezoelectric actuators during the scanning process. To address this issue, a spiral scanning path method is proposed in this paper. The block is used as the smallest scanning unit, with overlapping scanning parts between adjacent blocks, allowing for real-time calculation and compensation of distortions. Utilising the spiral scanning path method, compared with the formerly proposed correlation scanning method, a strong correlation between the blocks from the beginning to the end of the scanning process, effectively reducing the accumulation of drift during the scanning process, thereby significantly improving the issue of image distortion. An evaluation method for distortion correction based on scanning images is also introduced in this paper, which can assess the effectiveness of the proposed scanning method. Experimental results confirm that the spiral path scanning method proposed significantly improves the distortion correction compared to traditional methods. When the width of the scanning image is 600 pixels, the distortion is reduced by 94.9%. The proposed spiral correlated scanning method can be applied to long-term precise scanning scenarios in atomic force microscopy.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"299 1","pages":"65-76"},"PeriodicalIF":1.5,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143970524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hierarchical reconstruction of three-dimensional porous media from a single two-dimensional image with multiscale entropy statistics","authors":"Dong Dong Chen,&nbsp;Xiao Rui Wang,&nbsp;Jiao Fen Nan","doi":"10.1111/jmi.13418","DOIUrl":"10.1111/jmi.13418","url":null,"abstract":"<p>Despite the development of 3D imaging technology, the reconstruction of three-dimensional (3D) microstructure from a single two-dimensional (2D) image is still a prominent problem. In this paper, we propose a hierarchical reconstruction method based on simulated annealing, which is named hierarchical simulated annealing method (HSA), with the multiscale entropy statistics as the morphological information descriptor to reconstruct its corresponding three-dimensional (3D) microstructure from a single two-dimensional (2D) image. Both hierarchical simulated annealing (HSA) method and simulated annealing (SA) method are used to perform on the 2D and 3D microstructure reconstruction from a single 2D image, where the two-point cluster function and the standard two-point correlation function are used as the measurement metrics for the reconstructed 2D and 3D structures. From the 2D reconstructions, it can be seen that all the reconstructions of HSA method and SA method not only captures the similar morphological information with the original images, but also have a good agreement with the target microstructures in two-point cluster function. For the reconstructed 3D microstructures, the comparison of two-point correlation function shows that both HSA method and SA method can effectively reconstruct its 3D microstructure and the comparison of the reconstruction time between HSA method and SA method shows that the reconstruction speed of HSA method is an order of magnitude faster than that of SA method.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"299 1","pages":"49-64"},"PeriodicalIF":1.5,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143972011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CellPhePy: A python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos.","authors":"Laura Wiggins, Stuart Lacy, Graeme Park, Joanne Marrison, Ben Powell, Beth Cimini, Peter O'Toole, Julie Wilson, William J Brackenbury","doi":"10.1111/jmi.13416","DOIUrl":"https://doi.org/10.1111/jmi.13416","url":null,"abstract":"<p><p>We previously developed the CellPhe toolkit, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core functionality of the original toolkit, including single-cell phenotypic feature extraction, time-series analysis, feature selection and cell type classification. In addition, CellPhePy introduces significant enhancements, such as an improved method for identifying features that differentiate cell populations and extended support for multiclass classification, broadening its analytical capabilities. Notably, the CellPhePy package supports CellPose segmentation and TrackMate tracking, meaning that a set of microscopy images are the only required input with segmentation, tracking and feature extraction fully automated for downstream analysis, without reliance on external applications. The workflow's increased flexibility and modularity make it adaptable to different imaging modalities and fully customisable to address specific research questions. CellPhePy can be installed via PyPi or GitHub, and we also provide a CellPhePy GUI to aid user accessibility.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subcellular localisation and identification of single atoms using quantitative scanning transmission electron microscopy","authors":"A. A. Sheader,&nbsp;G Vizcay-Barrena,&nbsp;R. A. Fleck,&nbsp;S. J. L. Flatters,&nbsp;P. D. Nellist","doi":"10.1111/jmi.13410","DOIUrl":"10.1111/jmi.13410","url":null,"abstract":"&lt;p&gt;Determining the concentration of elements in subcellular structures poses a significant challenge. By locating an elemental species at high spatial resolution and with subcellular context, and subsequently quantifying it on an absolute scale, new information about cellular function can be revealed. Such measurements have not as yet been realised with existing techniques due to limitations on spatial resolution and inherent difficulties in detecting elements present in low concentrations. In this paper, we use scanning transmission electron microscopy (STEM) to establish a methodology for localising and quantifying high-&lt;i&gt;Z&lt;/i&gt; elements in a biological setting by measuring elastic electron scattering. We demonstrate platinum (Pt) deposition within neuronal cell bodies following in vivo administration of the Pt-based chemotherapeutic oxaliplatin to validate this novel methodology. For the first time, individual Pt atoms and nanoscale Pt clusters are shown within subcellular structures. Quantitative measurements of elastic electron scattering are used to determine absolute numbers of Pt atoms in each cluster. Cluster density is calculated on an atoms-per-cubic-nanometre scale, and used to show clusters form with densities below that of metallic Pt. By considering STEM partial scattering cross-sections, we determine that this new approach to subcellular elemental detection may be applicable to elements as light as sodium.&lt;/p&gt;&lt;p&gt;&lt;b&gt;LAY DESCRIPTION&lt;/b&gt;: Heterogeneous elemental distributions drive fundamental biological processes within cells. While carbon, hydrogen, oxygen and nitrogen comprise by far the majority of living matter, concentrations and locations of more than a dozen other species must also be tightly controlled to ensure normal cell function. Oxaliplatin is a first-line and adjuvant treatment for colorectal cancer. However, pain in the body's extremities (fingers and toes) significantly impairs clinical usage as this serious and persistent side effect impacts on both patient cancer care and quality of life. Annular dark-field (ADF) imaging in the scanning transmission electron microscope (STEM) provides an image with strong atom-number contrast and is sufficient to distinguish between different cell types and different organelles within the cells of the DRG. We also show that Pt may be imaged at the single atom level and be localised at very high resolution while still preserving a degree of ultrastructural context. The intrinsic image contrast generated is sufficient to identify these features without the need for heavy metal stains and other extensive processing steps which risk disturbing native platinum distributions within the tissue. We subsequently demonstrate that by considering the total elastic scattering intensity generated by nanometre-sized Pt aggregations within the cell, the ADF STEM may be used to make a measurement of local concentration of Pt in units of atoms per cubic nanometre. We further estimate the minimum ato","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"299 1","pages":"36-48"},"PeriodicalIF":1.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.13410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TOC - Issue Information","authors":"","doi":"10.1111/jmi.13326","DOIUrl":"https://doi.org/10.1111/jmi.13326","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"298 2","pages":"119-120"},"PeriodicalIF":1.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.13326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of the delayed wash (deglycerolisation) on the red blood cell morphology: Comparison of AFM and optical profilometry.","authors":"Natalia R Lobanova, Nadezda A Dolzhenkova, Elena V Boyakova, Olga A Maiorova, Anastasia A Frolova, Svetlana L Kotova, Yuri M Efremov, Peter S Timashev","doi":"10.1111/jmi.13415","DOIUrl":"https://doi.org/10.1111/jmi.13415","url":null,"abstract":"<p><p>The morphological characterisation is crucial for analysing cell states, especially for red blood cells (RBCs), which are used in transfusions. This study compared the applicability of atomic force microscopy (AFM) and confocal optical profilometry in the accurate characterisation of the RBC morphological parameters. The imaging of RBCs thawed after cryopreservation with immediate and delayed washing steps (deglycerolisation) was performed, and the morphological data obtained with AFM and optical profilometry were compared with the clinical laboratory studies. Both techniques provided close data on the morphological parameters, but optical profilometry allowed a faster and more convenient data acquisition. However, the membrane roughness analysis on discocytes and the submembrane cytoskeleton analysis on RBC ghosts was only possible with AFM due to its higher spatial resolution. Both techniques confirmed that delayed washing did not have negative effects on cells compared to immediate washing. Additional 3-day storage of both types of RBCs resulted in increased haemolysis. A decrease in the fraction of area occupied by pores in the submembrane cytoskeleton with the storage time was observed, possibly associated with the cytoskeleton deterioration. The studied conditions model the transportation of thawed RBCs in a cryoprotectant solution to medical facilities that have technical conditions to wash thawed RBCs and confirm its feasibility.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning assisted high-resolution microscopy image processing for phase segmentation in functional composite materials.","authors":"Ganesh Raghavendran, Bing Han, Fortune Adekogbe, Shuang Bai, Bingyu Lu, William Wu, Minghao Zhang, Ying Shirley Meng","doi":"10.1111/jmi.13413","DOIUrl":"https://doi.org/10.1111/jmi.13413","url":null,"abstract":"<p><p>In the domain of battery research, the processing of high-resolution microscopy images is a challenging task, as it involves dealing with complex images and requires a prior understanding of the components involved. The utilisation of deep learning methodologies for image analysis has attracted considerable interest in recent years, with multiple investigations employing such techniques for image segmentation and analysis within the realm of battery research. However, the automated analysis of high-resolution microscopy images for detecting phases and components in composite materials is still an underexplored area. This work proposes a novel workflow for FFT-based segmentation, periodic component detection and phase segmentation from raw high-resolution Transmission Electron Microscopy (TEM) images using a trained U-Net segmentation model. The developed model can expedite the detection of components and their phase segmentation, diminishing the temporal and cognitive demands associated with scrutinising an extensive array of TEM images, thereby mitigating the potential for human errors. This approach presents a novel and efficient image analysis approach with broad applicability beyond the battery field and holds potential for application in other related domains characterised by phase and composition distribution, such as alloy production.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating iterative ptychography with an integrated neural network.","authors":"Arthur R C McCray, Stephanie M Ribet, Georgios Varnavides, Colin Ophus","doi":"10.1111/jmi.13407","DOIUrl":"https://doi.org/10.1111/jmi.13407","url":null,"abstract":"<p><p>Electron ptychography is a powerful and versatile tool for high-resolution and dose-efficient imaging. Iterative reconstruction algorithms are powerful but also computationally expensive due to their relative complexity and the many hyperparameters that must be optimised. Gradient descent-based iterative ptychography is a popular method, but it may converge slowly when reconstructing low spatial frequencies. In this work, we present a method for accelerating a gradient descent-based iterative reconstruction algorithm by training a neural network (NN) that is applied in the reconstruction loop. The NN works in Fourier space and selectively boosts low spatial frequencies, thus enabling faster convergence in a manner similar to accelerated gradient descent algorithms. We discuss the difficulties that arise when incorporating a NN into an iterative reconstruction algorithm and show how they can be overcome with iterative training. We apply our method to simulated and experimental data of gold nanoparticles on amorphous carbon and show that we can significantly speed up ptychographic reconstruction of the nanoparticles.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to special issue on ‘Microscopy Techniques’","authors":"Stefan Linder","doi":"10.1111/jmi.13414","DOIUrl":"10.1111/jmi.13414","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"298 2","pages":"121-122"},"PeriodicalIF":1.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}