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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]

Accurately detecting and measuring structures in fluorescence microscopy images is important yet challenging. One of the interests of the Culley lab is to develop image analysis techniques to help researchers with these tasks. The aim of this summer project was to acquire images of fluorescently labelled tubulin in Schizosaccharomyces pombe cells and to produce an image processing pipeline adapted to quantifying the microtubules in these images. One approach to detecting these linear structures was based on thresholding and skeletonisation, using plugins available in Fiji/Imagel. This workflow yielded generally satisfactory results in the simplest case but was prone to creating artefacts and to loss of information. Crucially, this method could not cope with intersecting lines. As a more powerful alternative, the linear Hough transform was used to detect straight lines. The implementation in Python was efficient and overcame some of the drawbacks of the previous method. However, it required significant post-processing to faithfully detect multiple lines in images. Unfortunately, it was only feasible to parametrise simpler instances of straight lines, and the method could not be extended to e.g., curved lines. Deep learning approaches could be an exciting front for future work on this problem. [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]

[Display omitted] • A total of 13 different edible and non-edible oils are tested under image processing. • 1300 images are captured under a fluorescent lamp with CANON-EOS-200D digital camera. • KNN and SVM models are used for validation. • High Accuracy RGB image pixel extraction process with Fiji. • Significant properties of oils are predicted accurately. Renewable oils from different feedstocks are considered a potential replacement for the current petroleum products, and they are widely used in various applications like alternative fuels to heat engines and bio-coolants for metal removal operations, and their analysis of significant physicochemical properties is significant before their application. This study attempts to analyze the significant oil properties of different edible and non-edible oils using an image processing technique. For this purpose, 11 different oils are considered for experimentation, and their surface images of all the oils are captured and utilized to extract significant pixel information of Red, Green and Blue (RGB). The RGB pixel information is correlated with experimental properties (viscosity, density, flash point, fire point, cloud and pour points) and the developed model is validated with KNN, SVM and Fiji techniques and observed a high prediction accuracy (99%, 90.9% and 99%). Finally, two different oils which are not used in the experiment are chosen to test the prediction accuracy of the developed model. The results show that the RGB pixel predicts the significant oil properties, and they are validated experimentally and observed that the developed model predicts the oil properties with 91.7% accuracy. [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]