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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]

Uncovering the underdrawings (UDs), the preliminary sketch made by the painter on the grounded preparatory support, is a keystone for understanding the painting's history including the original project of the artist, the pentimenti (an underlying image in a painting providing evidence of revision by the artist) or the possible presence of co-workers' contributions. The application of infrared reflectography (IRR) has made the dream of discovering the UDs come true: since its introduction, there has been a growing interest in the technology, which therefore has evolved leading to advanced instruments. Most of the literature either report on the technological advances in IRR devices or present case studies, but a straightforward method to improve the visibility of the UDs has not been presented yet. Most of the data handling methods are devoted to a specific painting or they are not user-friendly enough to be applied by non-specialized users, hampering, thus, their widespread application in areas other than the scientific one, e.g., in the art history field. We developed a computer-assisted method, based on principal component analysis (PCA) and image processing, to enhance the visibility of UDs and to support the art-historians and curators' work. Based on ImageJ/Fiji, one of the most widespread image analysis software, the algorithm is very easy to use and, in principle, can be applied to any multi- or hyper-spectral image data set. In the present paper, after describing the method, we accurately present the extraction of the UD for the panel "The Holy Family with St. Anne and the Young St. John" and for other four paintings by Luini and his workshop paying particular attention to the painting known as "The Child with the Lamb". [ABSTRACT FROM AUTHOR]

Image processing techniques, including the Analyze Particles tool offered by Fiji software and the Intensity Profile by ICY (IP-ICY), were applied in core and outcrop examples to improve characterization of autocomposite ichnofabrics. Analyze Particles gives information about particle shape and size in the studied image. This tool was applied to Chondrites assemblages in composite ichnofabrics in view of selected images of modern marine hemipelagic cores from Site U1385 of IODP Expedition 339. Differences in size, relative abundance, density and distribution of Chondrites were interpreted as related to variations within the population of Chondrites tracemakers. Intensity Profile quantifies pixel values of the infilling material of traces, proving helpful to discriminate between specimens, evaluate the horizon of colonization, and approach the penetration depth. Its application to the analysis of Zoophycos improves characterization of composite ichnofabrics from deep-sea pelagic calcilutites of the Petra Tou Romiou section (Eocene Lefkara Formation; southern Cyprus). Some suites of Zoophycos were interpreted as associated with different phases of colonization from several horizons. Moreover, it was possible to discriminate structures pertaining to different specimens, as opposed to those from the same specimen. [ABSTRACT FROM AUTHOR]

In this study, an eco‐friendly fast simple method was developed for simultaneous determination of norfloxacin and tinidazole based on thin‐layer chromatography and image‐processing analysis. The binary mixture was separated using reversed phase ‐ thin layer chromatography plates and 30% trifluoroacetic acid only as mobile phase. Mobile phase composition was optimized using Taguchi orthogonal array and Derringer's desirability function. The plates were viewed under UV lamp and photographed by iPhone camera followed by image processing with Fiji software using integrated density as the measured response. As decreasing illumination increases the sensitivity of the method, this method was applied on two different ranges for each drug. The first one was 0.6–6.0 and 0.9–9.0 µg/spot for norfloxacin and tinidazole, respectively measured on the original image with normal illumination. The second one was measured after decreasing the illumination of the captured images at 0.06–0.60 and 0.09–0.90 µg/spot for norfloxacin and tinidazole, respectively. The proposed method was successfully applied for the determination of both drugs in tablets dosage form without interference from the commonly encountered excipients. Analytical Eco‐Scale was used to evaluate the greenness profile of the proposed method and it was found to be excellent green analytical method. [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]

Computer-assisted 4D manual cell tracking has been a valuable method for understanding spatial-temporal dynamics of embryogenesis (e.g., Stach & Anselmi BMC Biol, 13(113), 1–11 2015; Vellutini et al. BMC Biol, 15(33), 1–28 2017; Wolff et al. eLife, 7, e34410 2018) since the method was introduced in the late 1990s. Since two decades SIMI® BioCell (Schnabel et al. Dev Biol, 184, 234–265 1997), a software which initially was developed for analyzing data coming from the, at that time new technique of 4D microscopy, is in use. Many laboratories around the world use SIMI BioCell for the manual tracing of cells in embryonic development of various species to reconstruct cell genealogies with high precision. However, the software has several disadvantages: limits in handling very large data sets, the virtually no maintenance over the last 10 years (bound to older Windows versions), the difficulty to access the created cell lineage data for analyses outside SIMI BioCell, and the high cost of the program. Recently, bioinformatics, in close collaboration with biologists, developed new lineaging tools that are freely available through the open source image processing platform Fiji. Here we introduce a software tool that allows conversion of SIMI BioCell lineage data to a format that is compatible with the Fiji plugin MaMuT (Wolff et al. eLife, 7, e34410 2018). Hereby we intend to maintain the usability of SIMI BioCell created cell lineage data for the future and, for investigators who wish to do so, facilitate the transition from this software to a more convenient program. [ABSTRACT FROM AUTHOR]

In recent years, image treatment has been appraised as a very powerful tool to facilitate ichnological analysis, especially in marine cores of modern sediments, supporting the determination of certain ichnological features. However, it is still a new approach and detailed research is necessary to encounter a faster and more efficient method. The present study focuses on two image processing techniques, Fiji and ICY, and their comparison with a refined version of the well-established high-resolution image treatment. Strengths and weaknesses of the methodologies for the determination of three main features were explored: i) visibility of trace fossils; ii) quantification of the percentage of bioturbated surface, and iii) penetration depth estimation. Refined high-resolution image treatment gives the best results for enhanced visibility of trace fossils, whereas Fiji is found to be a sound and rapid option. One disadvantage shared by Fiji and ICY is the binary character of the produced images, which may impede later ichnotaxonomical differentiation. Both Fiji and ICY (+ Fiji) are rapid alternatives for quantifying the bulk amount of bioturbated surface. The Magic Wand Method (+ RefineEdge), based on high-resolution image treatment, provides good results regardless of the contrast of the images, and it additionally allows for a more detailed quantification. The semi-automatic character of ICY favors quick estimation of penetration depth and facilitates differentiation between distinct tracemaker communities, based on a rapid quantification of pixel values. Thus, Fiji and ICY methods offer good results and are much less time-consuming than high-resolution image treatment. They are proposed as faster alternatives for the estimation of ichnological features, especially useful at the beginning stages of research, when a large number of samples must be analyzed. • Image treatment methods have been proved in ichnological research • High-resolution image treatment RefineEdge is the best to increase visibility • Fiji (CLAHE) is a rapid alternative for preliminary analysis of cores • Fiji and ICY (+ Fiji) is a rapid alternatives to quantify bioturbated surface • Intensity Profile plugin by ICY are useful for estimating penetration depth. [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]

• Deep learning models often require large volumes of labelled data. • The labelling process is usually very labour intensive. • We demonstrate a method of generating and labelling B-mode ultrasound images automatically. • This approach may have applications in image processing and data sharing. Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches. We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitudinal images of the gastrocnemius medialis muscle, and a set of 100 synthetic segmented masks that featured two aponeuroses and a random number of 'fascicles'. The model output a set of synthetic ultrasound images and an automated segmentation of each real input image. This automated segmentation process was one of the two approaches we assessed. The second approach involved synthesising ultrasound images and then feeding these images into an ImageJ/Fiji-based automated algorithm, to determine whether it could detect the aponeuroses and muscle fascicles. Histogram distributions were similar between real and synthetic images, but synthetic images displayed less variation between samples and a narrower range. Mean entropy values were statistically similar (real: 6.97, synthetic: 7.03; p = 0.218), but the range was much narrower for synthetic images (6.91 – 7.11 versus 6.30 – 7.62). When comparing GAN-derived and manually labelled segmentations, intersection-over-union values- denoting the degree of overlap between aponeurosis labels- varied between 0.0280 – 0.612 (mean ± SD: 0.312 ± 0.159), and pennation angles were higher for the GAN-derived segmentations (25.1° vs. 19.3°; p < 0.001). For the second segmentation approach, the algorithm generally performed equally well on synthetic and real images, yielding pennation angles within the physiological range (13.8–20°). We used a GAN to generate realistic B-mode ultrasound images, and extracted muscle architectural parameters from these images automatically. This approach could enable generation of large labelled datasets for image segmentation tasks, and may also be useful for data sharing. Automatic generation and labelling of ultrasound images minimises user input and overcomes several limitations associated with manual analysis. [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]