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Premise: Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R‐based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities. Methods and Results: We evaluated CuticleTrace‐generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50‐image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand‐traced measurements, outperforming alternate methods. Conclusions: CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high‐throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high‐quality cell shape data sets. [ABSTRACT FROM AUTHOR]

Motivation: Quantitative descriptions of multi-cellular structures from optical microscopy imaging are prime to understand the variety of three-dimensional (3D) shapes in living organisms. Experimental models of vertebrates, invertebrates and plants, such as zebrafish, killifish, Drosophila or Marchantia, mainly comprise multilayer tissues, and even if microscopes can reach the needed depth, their geometry hinders the selection and subsequent analysis of the optical volumes of interest. Computational tools to "peel" tissues by removing specific layers and reducing 3D volume into planar images, can critically improve visualization and analysis. Results: We developed VolumePeeler, a versatile FIJI plugin for virtual 3D "peeling" of image stacks. The plugin implements spherical and spline surface projections. We applied VolumePeeler to perform peeling in 3D images of spherical embryos, as well as non-spherical tissue layers. The produced images improve the 3D volume visualization and enable analysis and quantification of geometrically challenging microscopy datasets. Availability: ImageJ/FIJI software, source code, examples, and tutorials are openly available in https://cimt.uchile.cl/mcerda [ABSTRACT FROM AUTHOR]

Traditionally histology is the gold standard for the validation of imaging experiments. Matching imaging slices and histological sections and the precise outlining of corresponding tissue structures are difficult. Challenges are based on differences in imaging and histological slice thickness as well as tissue shrinkage and alterations after processing. Here we describe step-by-step instructions that might be used as a universal pathway to overlay MRI and histological images and for a correlation of measurements between imaging modalities. The free available (Fiji is just) ImageJ software tools were used for regions of interest transformation (ROIT) and alignment using a rat brain MRI as an example. The developed ROIT procedure was compared to a manual delineation of rat brain structures. The ROIT plugin was developed for ImageJ to enable an automatization of the image processing and structural analysis of the rodent brain. [ABSTRACT FROM AUTHOR]

The image processing of muscular fibers has seen significant improvements over the last decades, from light microscopy acquisition to virtual microscopy and from manual segmentation and fiber typing to automatic. The present study aims to discuss the main tools employed in different histo-enzymological image processing phases applied to muscle fibers, which are respectively: the acquisition, segmentation and fiber typing as well as their efficiency in the determination of morphometric parameters. First, the acquisition: to count the cell numbers within the muscle fiber sections, optical microscopic images with different magnifications (x100, x200, x250, and x400) were compared with virtual slides digitized by Slide Scanner. Second, the segmentation: Three softwares (Fiji "Digitizing pen, Mouse", Image Pro Plus 10 [semi-automatic], and Cytoinformatics LLC [automatic]) were compared for image segmentation quality and the determining of fiber numbers. Third, manual fiber typing: employing Fiji of segmented images using the 3 previously mentioned softwares were performed to calculate the accuracy of the morphometric parameter (Cross sectional Area [CSA], perimeter and Minimal Feret's Diameter [MFD]). The results of the acquisition showed that the scanner slides have a better resolution and detected a higher number of cells than when optical microscopes with different magnifications were used; with the latter, the processing can be performed only with an acceptable resolution, where the number of cells is lesser, which unfortunately requires several repetitions and exhausting work. Our findings regarding segmentation indicate that the Cytoinformatics LLC showed the best processing time and the highest quality followed by IP and Fiji. Finally and for the ultimate step, the morphometric parameter calculation: showed that the best accuracy was attained by using Fiji followed by Cytoinformatics and finally by IP. The findings of this study suggest that Fiji (semi-automatic) showed the best quality/price ratio (open access software) for segmentation and fiber typing, but it proved to be time consuming when compared to Image Pro Plus 10 (semi-automatic) and Cytoinformatics LLC (automatic). [ABSTRACT FROM AUTHOR]

Summary Creating 3D animations from microscopy data is computationally expensive and requires high-end hardware. We therefore developed 3Dscript.server, a 3D animation software that runs as a service on dedicated, shared workstations. Using 3Dscript as the underlying rendering engine, it offers unique features not found in existing software: rendering is performed completely server-side. The target animation is specified on the client without the rendering engine, eliminating any hardware requirements client-side. Still, defining an animation is intuitive due to 3Dscript's natural language-based animation description. We implemented a new OMERO web app to utilize 3Dscript.server directly from the OMERO web interface; a Fiji client to use 3Dscript.server from Fiji for integration into image processing pipelines; and batch scripts to run 3Dscript.server on compute clusters for large-scale visualization projects. Availability and implementation Source code and documentation is available at https://github.com/bene51/omero%5f3Dscript , https://github.com/bene51/3Dscript.server and https://github.com/bene51/3Dscript.cluster. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary Here we introduce a Fiji plugin utilizing the HPC-as-a-Service concept, significantly mitigating the challenges life scientists face when delegating complex data-intensive processing workflows to HPC clusters. We demonstrate on a common Selective Plane Illumination Microscopy image processing task that execution of a Fiji workflow on a remote supercomputer leads to improved turnaround time despite the data transfer overhead. The plugin allows the end users to conveniently transfer image data to remote HPC resources, manage pipeline jobs and visualize processed results directly from the Fiji graphical user interface. Availability and implementation The code is distributed free and open source under the MIT license. Source code: https://github.com/fiji-hpc/hpc-workflow-manager/ , documentation: https://imagej.net/SPIM%5fWorkflow%5fManager%5fFor%5fHPC. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Ni-Al alloys create a cone- shaped figure when there is internal oxide. This behavior was studied by TEM, SEM, X-Ray (XRD), Optical Microscopy and Image Processing. The internal oxide precipitates and its results indicate that this precipitation forms continuous rods in a cone-shaped configuration extending from the surface to the internal oxide front for Ni-Al alloys, whereas for Ni-X (X = Cr, Mo, V, W and Mn) alloys the precipitation is discrete and more irregularly-shaped. Furthermore, in a high atomic percentage (5.18% to 8.67%), the precipitation was rod-like and continuous from the surface to the internal oxide front for all temperature\time conditions. For the Ni-2.47% Al alloy at 800 °C, observations showed a mixture of rod-like and fork like precipitates, whereas after oxidation at 1000 and 1100 °C the precipitation was rod-like and continuous. For the Ni-1.18% Al alloy the aluminum concentration was insufficient for fully continuous precipitation to develop, and the internal oxides were generally acicular-shaped and discontinuous. Images obtained by TEM and, after that, analyzed by image processing allowed us to understand their behavior and the internal oxide patterns. [ABSTRACT FROM AUTHOR]

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization.  Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible "cheat sheet"-style format, enabling wide distribution, use, and adoption to more specific needs., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Schmied C and Jambor HK.)