Journal of microscopy最新文献_第6页

Supervised artificial intelligence classification of clinker processing conditions by optical microscopy. 光学显微镜下熟料处理条件的监督人工智能分类。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-17 DOI: 10.1111/jmi.70150

Daniela Gastaldi, Mirko Ippolito, Fulvio Canonico, Luca Contò, Enrico Boccaleri, Marco Dossena, Christopher Irwin, Stefano Nera, Luca Piovesan

引用次数: 0

Revealing buried layers in oxide heterostructures with scanning transmission electron microscopy using integrated differential phase contrast (STEM-iDPC) imaging. 利用集成差相衬（STEM-iDPC）成像的扫描透射电子显微镜揭示氧化物异质结构中的埋藏层。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-15 DOI: 10.1111/jmi.70149

Deepak Kumar Dinkar, Sebastian C Dixon, Ivor Guiney, Christopher S Allen, Colin Humphreys

{"title":"Revealing buried layers in oxide heterostructures with scanning transmission electron microscopy using integrated differential phase contrast (STEM-iDPC) imaging.","authors":"Deepak Kumar Dinkar, Sebastian C Dixon, Ivor Guiney, Christopher S Allen, Colin Humphreys","doi":"10.1111/jmi.70149","DOIUrl":"https://doi.org/10.1111/jmi.70149","url":null,"abstract":"Atomic-scale characterisation of thin buried layers in oxide heterostructures is often limited by the reduced sensitivity of conventional Z-contrast high-angle annular dark-field (HAADF) imaging to light elements and low-density regions. Here, a Sc2O3/Er2O3/Si heterostructure was investigated using aberration-corrected scanning transmission electron microscope (STEM) with simultaneous HAADF and integrated differential phase contrast (iDPC) imaging, with site-specific cross-sectional specimens prepared by scanning electron microscope-plasma focused ion beam (SEM-PFIB) using a Xe-ion beam under identical acquisition conditions to enable direct contrast comparison. Although HAADF clearly resolves the heavy rare-earth oxide layers and reveals a broad interfacial region with contrast variations indicative of structural complexity, simultaneous STEM-iDPC imaging resolves a distinct ∼3 nm buried interfacial layer with substantially greater clarity. Additional high-resolution transmission electron microscopy (HRTEM) and electron energy-loss spectroscopy (EELS) analyses reveal oxygen enrichment and local structural ordering within this region, indicating that it is chemically and structurally distinct from both crystalline Si and bulk Er2O3. The combined observations are consistent with an oxygen-rich Er-Si-O transition layer. These results demonstrate the capability of STEM-iDPC to reveal buried interfacial structure and highlight the value of combining HAADF, iDPC, HRTEM and spectroscopy for comprehensive characterisation of oxide heterostructures.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148447690","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}

引用次数: 0

Automated quantification of drug-induced cell death via vacuolation scoring in microscopic images. 通过显微镜图像中的空泡化评分自动量化药物诱导的细胞死亡。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-13 DOI: 10.1111/jmi.70128

Navaneeth Krishna S, Sweata Hanson, Divya Nedungadi, Nandita Mishra, G Gopakumar

{"title":"Automated quantification of drug-induced cell death via vacuolation scoring in microscopic images.","authors":"Navaneeth Krishna S, Sweata Hanson, Divya Nedungadi, Nandita Mishra, G Gopakumar","doi":"10.1111/jmi.70128","DOIUrl":"https://doi.org/10.1111/jmi.70128","url":null,"abstract":"Cell death plays a critical role in maintaining cellular homeostasis. The accurate evaluation of cell death is essential to determine the efficacy of a drug. Understanding the extent of cell death helps assess the potency of the chosen drug, which is a critical aspect of drug discovery research. The traditional method of verifying drug efficacy manually by observing the cells after drug application is time-consuming, labour-intensive, and prone to human error, highlighting the need for more precise and automated approaches. We introduce here the relevance of detecting paraptosis - a novel, non-apoptotic form of programmed cell death characterised by extensive cytoplasmic vacuolation. Non-small cell lung cancer cells (A549) treated with ginger extract (GE), which is known to induce paraptosis, were used as the model system for this study. The rate of cell death induced by GE is found to increase in a dose- and time-dependent manner. The cytoplasmic vacuolation is due to dilation of endoplasmic reticulum (ER) with excessive ER stress and downregulation of Alix, a paraptosis marker. In this work, we propose an automated solution that exploits recent advances in artificial intelligence to effectively segment and isolate individual cells, and to quantify their textural, morphological, and statistical characteristics. These are then used by a machine learning classifier to predict the cell death score. Such a score can be used effectively for drug screening. The suggested framework integrates Mask R-CNN for cell segmentation and an XGBoost classifier for evaluating the segmented areas. Therefore, this automated system can be a valuable tool to assess the degree of vacuolation formed while screening for paraptosis-inducing drugs, thereby providing insights into the level of cytotoxicity. The approach has the potential to enhance the scalability and robustness of automated drug screening and possible biomedical image analysis to a great extent.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430698","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}

引用次数: 0

Bioimage analysis in deep visual proteomics: Advancing transparency, reproducibility, and FAIR principles. 深度视觉蛋白质组学中的生物图像分析：推进透明度，可重复性和公平原则。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-13 DOI: 10.1111/jmi.70145

Devon Siemes, Angelo Novak, Stephanie Thiebes, Lars Widera, Stephanie Tautges, Hannah Voss, Lars Borgards, Dominik Habermann, Jianxu Chen, Olga Shevchuk, Daniel R Engel

{"title":"Bioimage analysis in deep visual proteomics: Advancing transparency, reproducibility, and FAIR principles.","authors":"Devon Siemes, Angelo Novak, Stephanie Thiebes, Lars Widera, Stephanie Tautges, Hannah Voss, Lars Borgards, Dominik Habermann, Jianxu Chen, Olga Shevchuk, Daniel R Engel","doi":"10.1111/jmi.70145","DOIUrl":"https://doi.org/10.1111/jmi.70145","url":null,"abstract":"Deep Visual Proteomics (DVP) combines high-resolution microscopy, computational instance segmentation, laser capture microdissection (LMD), and ultrasensitive mass spectrometry to enable spatially resolved molecular analysis of defined cellular populations. Within this workflow, LMD plays a central role by physically isolating microscopy-defined regions of interest for downstream proteomic profiling. However, accurate image-to-stage transfer remains challenging due to limitations in imaging resolution, tissue preparation variability, laser cutting parameters, and partially manual alignment procedures. Here, we present an open-source semi-automated image-to-LMD interoperability framework for DVP that integrates fiducial marker detection, coordinate transformation, and segmentation-driven contour export for reproducible microdissection. Using neutrophil isolation from murine urinary bladder tissue as a representative use case, we demonstrate robust alignment of computationally defined cellular boundaries with LMD-guided excision. By improving interoperability, transparency, and workflow standardisation, this framework strengthens the integration of microscopy and spatial proteomics and supports reproducible analysis of spatially defined immune cell populations.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148430683","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}

引用次数: 0

Reproducibility assessment of a published cell shape transition analysis workflow. 已发布的细胞形状转换分析工作流程的再现性评估。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-08 DOI: 10.1111/jmi.70143

Esteban A Miglietta, An-Chi Luo, Agustin A Corbat, Shao-Chun Hsu

{"title":"Reproducibility assessment of a published cell shape transition analysis workflow.","authors":"Esteban A Miglietta, An-Chi Luo, Agustin A Corbat, Shao-Chun Hsu","doi":"10.1111/jmi.70143","DOIUrl":"https://doi.org/10.1111/jmi.70143","url":null,"abstract":"Image quantification is central to modern biomedical research. However, the reproducibility of image-based studies remains a persistent challenge due to limited access to raw data and incomplete reporting of analysis workflows. To assess these issues, the Global Bioimage Analyst Society (GloBIAS) launched a coordinated effort in 2024 to examine published bioimage analysis workflows. Here, we evaluated the reproducibility of a published workflow for analysing cell shape transitions in Dictyostelium discoideum. While the reported kymographs and biological conclusions could be qualitatively reproduced, a critical upstream step - cell segmentation - was insufficiently described. Reproducing the segmentation step based on the available information required extensive parameter tuning and failed to consistently capture accurate cell contours, thereby compromising downstream analysis. To evaluate whether the downstream analytical components could be reproduced when segmentation quality was improved, we adopted an alternative segmentation strategy using ilastik, a machine learning-based image analysis tool. Using cell-boundary coordinates extracted from ilastik-based masks as alternative inputs for downstream analysis, we reconstructed kymographs that partially matched the published results. Our findings demonstrate that accessible software and raw data alone are insufficient to ensure reproducibility in image-based research. Complete reporting of image-processing workflows, including segmentation strategies and parameter settings, is essential for the reliable reproduction of published quantitative analyses.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148404966","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}

引用次数: 0

Nanoprecipitate characterisation in NiTi and aluminium alloys: A TEM-based overview. 纳米沉淀表征镍钛和铝合金：基于tem的概述。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-07 DOI: 10.1111/jmi.70134

Seyed Aref Golsorkhi, Opemipo Adetan, Melodie Fickenscher, Dinc Erdeniz

{"title":"Nanoprecipitate characterisation in NiTi and aluminium alloys: A TEM-based overview.","authors":"Seyed Aref Golsorkhi, Opemipo Adetan, Melodie Fickenscher, Dinc Erdeniz","doi":"10.1111/jmi.70134","DOIUrl":"10.1111/jmi.70134","url":null,"abstract":"Nanoscale precipitates are central to the functional and mechanical performance of many advanced alloys, yet their characteristics and spatial distributions can be directly resolved only by electron microscopy. In this poster-style article, focused ion beam-prepared TEM lamellae are used to provide a microscopy-focused overview of nanoprecipitate populations in two representative precipitate-controlled systems: a Ni-rich NiTi shape memory alloy and a multicomponent Al-Ce-(Er,Sc,Zr) aluminium alloy. In the NiTi alloy, a short heat treatment at 560°C for 1 min produced a recovered, subgrained matrix containing lenticular Ni4Ti3 precipitates with nanoscale dimensions. Representative TEM observations show precipitates located within the matrix and near dislocation-rich regions and subgrain boundaries, suggesting a local spatial association with the deformation substructure. In the Al-Ce-(Er,Sc,Zr) alloy, TEM analysis of aged samples shows coarse Al11Ce3 intermetallic phases within an Al matrix containing fine nanoscale precipitates consistent with L12-Al3(Er,Sc,Zr) type precipitates. Selected-area electron diffraction (SAED) confirms the ordered L12 structure through superlattice reflections. Together, these case studies illustrate how FIB-TEM workflows enable direct visualisation of precipitate morphology, coherency, and spatial distribution across distinct alloy systems.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148396830","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}

引用次数: 0

BioImageIT: A novel python-based architecture for reproducible bio-image workflows. BioImageIT：一个新颖的基于python的架构，用于可重复的生物图像工作流。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-07-06 DOI: 10.1111/jmi.70141

Arthur Masson, Sylvain Prigent, Cesar Augusto Valades-Cruz, Ludovic Leconte, Léo Maury, Jean Salamero, Charles Kervrann

{"title":"BioImageIT: A novel python-based architecture for reproducible bio-image workflows.","authors":"Arthur Masson, Sylvain Prigent, Cesar Augusto Valades-Cruz, Ludovic Leconte, Léo Maury, Jean Salamero, Charles Kervrann","doi":"10.1111/jmi.70141","DOIUrl":"https://doi.org/10.1111/jmi.70141","url":null,"abstract":"Recent advances in light microscopy have transformed the scale and complexity of biological imaging data, creating an urgent need for sophisticated computational pipelines capable of extracting meaningful biological insights. However, the current software ecosystem for bio-image analysis remains highly fragmented, with researchers needing to integrate tools written in disparate programming languages and architectural paradigms. This fragmentation gives rise to 'dependency hell' and creates significant technical barriers for life scientists. We present the novel and flexible architecture of BioImageIT, a lightweight open-source workflow management system designed to bridge the gap between advanced computational tools and end-user bio-image analysts. Built upon Python, BioImageIT has evolved into a dual interface architecture: a node-based visual programming GUI alongside a comprehensive Python Application Programming Interface (API). The system features the Wetlands environment management system for automatic dependency resolution, and adopts pandas DataFrames as the universal data structure for inter-node communication. BioImageIT enforces adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) throughout the analysis lifecycle, automatically capturing comprehensive metadata for every processing step. The architecture abstracts the underlying computational infrastructure, laying the groundwork for seamless scaling from local workstations to high-performance computing (HPC) clusters-a capability currently under active development.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148391214","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}

引用次数: 0

In operando imaging of the space-charge region in a 4H-SiC MOSCAP using STEM-EBIC. 利用STEM-EBIC对4H-SiC MOSCAP的空间电荷区进行操作成像。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-06-29 DOI: 10.1111/jmi.70142

Eoin Moynihan, Arne Renz, Akif Yildirim, Peter Gammon, Richard Beanland, Ana M Sanchez

引用次数: 0

The future of DXA: How AI is transforming bone health diagnostics. DXA的未来：人工智能如何改变骨骼健康诊断。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-06-29 DOI: 10.1111/jmi.70140

Mahsa Shakeri, Seyyed Mohammad Hosseini, Nikolas Knowles, Forough Jamil, Mohammad Haghparast, Leili Darvish

{"title":"The future of DXA: How AI is transforming bone health diagnostics.","authors":"Mahsa Shakeri, Seyyed Mohammad Hosseini, Nikolas Knowles, Forough Jamil, Mohammad Haghparast, Leili Darvish","doi":"10.1111/jmi.70140","DOIUrl":"https://doi.org/10.1111/jmi.70140","url":null,"abstract":"Dual-energy X-ray absorptiometry (DXA) remains the clinical gold standard for assessing bone mineral density (BMD), guiding diagnosis and therapeutic decisions. However, conventional DXA analysis suffers from several limitations, including insensitivity to early microarchitectural changes, operator dependence, limited availability in primary care settings, and an insufficient ability to predict fracture risk when used alone. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers transformative potential in enhancing DXA-based bone health assessment. The purpose of this review is to describe the integration of AI algorithms into DXA image interpretation, highlighting improvements in diagnostic accuracy, and fracture risk stratification beyond traditional methods. Using AI-driven models, complex features of DXA images can be extracted, increasing sensitivity to microstructural deterioration that is typically not detected by standard BMD measurements. A combination of quantitative image features and comprehensive demographic and clinical data enhance the early detection of osteoporosis and fracture susceptibility, enabling personalised treatment strategies. In comparison to classical DXA or fracture risk assessment tool (FRAX) algorithms, convolutional neural networks (CNNs) and ensemble methods demonstrate superior predictive performance, with average area under the curve (AUC) values often about 0.90. In addition to minimising inter-operator variability and improving reproducibility, AI improves DXA technical challenges such as region-of-interest selection and image segmentation. In addition to providing indirect measurements of bone microarchitecture, AI-enabled indices, such as the trabecular bone score (TBS), contribute to the improvement of fracture risk prediction. It has been demonstrated in large-scale clinical validations that AI-assisted DXA can enhance bone health diagnostic capability.","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148345599","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}

引用次数: 0

The Origins of Ploem's Filter Cube: A Pandora's Box. Ploem的过滤器立方体的起源：潘多拉的盒子。

IF 1.9 4区工程技术

Journal of microscopy Pub Date : 2026-06-24 DOI: 10.1111/jmi.70137

Jack Gordon, Kaleah Balcomb, Vinod Maseedupally, Maitreyee Roy

引用次数: 0