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Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites.

Transmission electron microscopy (TEM) is widely used as an imaging modality to provide high-resolution details of subcellular components within cells and tissues. Mitochondria and endoplasmic reticulum (ER) are organelles of particular interest to those investigating metabolic disorders. A straightforward method for quantifying and characterizing particular aspects of these organelles would be a useful tool. In this protocol, we outline how to accurately assess the morphology of these important subcellular structures using open source software ImageJ, originally developed by the National Institutes of Health (NIH). Specifically, we detail how to obtain mitochondrial length, width, area, and circularity, in addition to assessing cristae morphology and measuring mito/endoplasmic reticulum (ER) interactions. These procedures provide useful tools for quantifying and characterizing key features of sub-cellular morphology, leading to accurate and reproducible measurements and visualizations of mitochondria and ER.

Cell migration is a key procedure involved in many biological processes including embryological development, tissue formation, immune defense or inflammation, and cancer progression. How physical, chemical, and molecular aspects can affect cell motility is a challenge to understand migratory cells behavior. assays are excellent approaches to extrapolate to situations and study live cells behavior. Here we present four protocols that describe step-by-step cell migration, invasion and adhesion strategies and their corresponding image data quantification. These current protocols are based on wound healing assays (comparing traditional pipette tip-scratch assay vs. culture insert assay), 2D individual cell-tracking experiments by live cell imaging and spreading and transwell assays. All together, they cover different phenotypes and hallmarks of cell motility and adhesion, providing orthogonal information that can be used either individually or collectively in many different experimental setups. These optimized protocols will facilitate physiological and cellular characterization of these processes, which may be used for fast screening of specific therapeutic cancer drugs for migratory function, novel strategies in cancer diagnosis, and for assaying new molecules involved in adhesion and invasion metastatic properties of cancer cells.

Quantitative immunofluorescence is widely used to assess molecular expression and cellular distribution across biological tissues, yet the analysis of large image datasets remains time-consuming and prone to user-dependent variability. To address these limitations, we herein developed a semi-automated workflow that integrates ImageJ/Fiji for image processing, StarDist for nuclear segmentation, and spreadsheet- or Python-based routines for data curation. The pipeline standardizes critical analytical steps, including scale calibration, region-of-interest (ROI) definition, channel selection, and z-stack handling, while preserving essential metadata through a structured file-naming system. Optical density and cell-number metrics are exported automatically in a consistent format, enabling efficient consolidation into a unified dataset. Subsequent curation can be performed either manually in a spreadsheet software or fully automatically through custom Python scripts, allowing extraction of sample identifiers, regions, and markers, as well as calculation of normalized intensity values. Comparison with existing protocols proved that this workflow adheres to widely accepted quantification principles while markedly improving reproducibility, consistency, and analytical throughput. This method offers a straightforward, transparent, and scalable solution for fluorescence-based quantification suitable for laboratories with varying levels of computational expertise.

[This corrects the article DOI: 10.3389/fcell.2023.1005145.].

As an essential characterization, size distribution is an important indicator for the synthesis, optimization, and application of nanoparticles. Electron microscopes such as transmission electron microscopes (TEMs) are commonly utilized to collect size information on nanoparticles. However, the current popular statistical method of manually measuring large particles one by one, using a ruler tool in the corresponding image analysis software is time-consuming and can introduce manual errors. Moreover, it is difficult to determine the measurement interval for irregularly shaped nanoparticles. Therefore, it is necessary to use an efficient and standard method to perform size distribution analysis of nanoparticles. In this work, we use basic ImageJ software (1.53 t) to analyze the size of typical silica nanoparticles in a TEM image and use Origin software to process the data, to obtain its accurate distribution quickly. Using it as a template, we believe that this work can provide a paradigm for the standardized analysis of nanoparticle size.

Background Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. Results We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Conclusions Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

During the capturing of the time-lapse sequence of fluorescently labeled samples, fluorescence intensity exhibits decays. This phenomenon is known as 'photobleaching' and is a widely known problem in imaging in life sciences. The photobleaching can be attenuated by tuning the imaging set-up, but when such adjustments only partially work, the image sequence can be corrected for the loss of intensity in order to precisely segment the target structure or to quantify true intensity dynamics. We implemented an ImageJ plugin that allows the user to compensate for the photobleaching to estimate the non-bleaching condition with choice of three different algorithms: simple ratio, exponential fitting, and histogram matching methods. The histogram matching method is a novel algorithm for photobleaching correction. This article presents details and characteristics of each algorithm based on application to actual image sequences.

Purpose To compare AngioTool (AT) vascular parameters (VP) between MacTel2 eyes and normal eyes. Secondary outcome measures were to correlate VP with BCVA and to analyze VP between various grades of Simple MacTel Classification. Methods This is a retrospective study. SD OCTA images of the superficial vascular complex (SVC) and deep capillary complex (DVC) were exported into Image J and AT. The explant area (EA), vessel area (VA), vessel percentage area (VPA), total number of junctions (TNJ), junction density (JD), total vessel length (TVL), average vessel length (AVL), total number of endpoints (TNE) and mean E lacunarity (MEL) were studied. Results Group 1 had 120 MacTel2 eyes. Group 2 had 60 age-matched normal eyes. All VP were significantly different between the two groups except EA and TNE in both complexes. None of the VP had a correlation with BCVA. Interquadrant analysis (IQA) in SVC and DVC showed statistical significance in VPA, AVL and JD and in AVL, TNE, JD, VPA respectively. Post hoc analysis in SVC and DVC showed statistical significance in TNJ, JD, TVL and AVL between grade 1 and grade 3, and in VA, VPA, TNJ, JD, TVL and MEL between grade 0 and grade 3 respectively. Conclusion VP were affected in MacTel2 eyes. VP did not correlate with BCVA. Occurrence of pigmentation is an important event in the progression of disease. AT may provide quantitative markers to measure disease progression.

 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the global problem of respiratory disease from 2019 to 2024. One of the earliest variations in the SARS-CoV-2 S protein was the S D614G mutation. SARS-CoV-2 has several important variants, namely, Alpha, Beta, Gamma, Delta, and Omicron. Omicron is the variant that has caused severe health problems, someresulting in death, in the elderly. Omicron has further differentiated to some wellknown variants, such as, BA.1, BA.2, BA.2.75, BA.5, BQ.1.1, and XBB.1. According to Japanese Government data, the number of citizens aged 65 years old and above reached 28.9% in 2021. From our previous experiment, antibodies of the elderly that have received four doses of mRNA vaccine still could not optimally neutralize Omicron BQ.1.1 and XBB.1. We aimed to analyze the plaque size of SARS-CoV-2 variants that caused severe COVID-19 in the elderly. SARS-CoV-2 variants were seeded in Vero E6-TMPRSS2 cell culture to create plaques. The resulting plaques were analyzed with ImageJ application to select solitary plaques and to determine plaque sizes. The size of BA.1 plaque was indifferent to BA.2 plaque. The plaque area comparison result was as follows, BA.1/BA.2

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