Open-source Image Analysis Platform Research Articles

Quantifying the reproductive progression of sunflower using FIJI (Image J)

Sunflower (Helianthus annuus L.) is today the third leading oilseed crop in the world and seed yield is a valuable trait for breeders and researchers. The sunflower capitulum is composed of 700 to 3000 individual flowers on a flattered receptacle. Most reproductive stages (R5 to R6) have at least two disc flower phenophase's coexisting in the same receptacle (E1 to E4). Today, researchers in agroecology and breeders manually quantify the number of disc flowers that achieve the anthesis at different developmental stages of the receptacle. The presented method applies a bioinformatic tool to estimate: (1) the number of disc flowers of each phenophase that are constituting the sunflower´s capitula at different reproductive stages and, (2) the number of developing seeds of each sunflower capitulum. The ImageJ software was used as an image-analysis tool on sunflower capitula photographs. A use case and method validation for each presented protocol is provided. This method will contribute to correlation analysis in agroecological studies and also would be useful for the early prediction of seed yield in breeding programs.•This is a simple method for the estimation of the number of disc flowers at each phenophase in the sunflower receptacle.•It is based on integrating the knowledge of sunflower reproductive development with an open-source image analysis platform applied in single workflows.•This is a precise, non-destructive, rapid, and low-cost method; thus, it has the potential to be adopted as a phenotyping tool for sunflower breeding and research in agroecology.

FDA approved L-type channel blocker Nifedipine reduces cell death in hypoxic A549 cells through modulation of mitochondrial calcium and superoxide generation

As hypoxia is a major driver for the pathophysiology of COVID-19, it is crucial to characterize the hypoxic response at the cellular and molecular levels. In order to augment drug repurposing with the identification of appropriate molecular targets, investigations on therapeutics preventing hypoxic cell damage is required. In this work, we propose a hypoxia model based on alveolar lung epithelial cells line using chemical inducer, CoCl2 that can be used for testing calcium channel blockers (CCBs). Since recent studies suggested that CCBs may reduce the infectivity of SARS-Cov-2, we specifically select FDA approved calcium channel blocker, nifedipine for the study. First, we examined hypoxia-induced cell morphology and found a significant increase in cytosolic calcium levels, mitochondrial calcium overload as well as ROS production in hypoxic A549 cells. Secondly, we demonstrate the protective behaviour of nifedipine for cells that are already subjected to hypoxia through measurement of cell viability as well as 4D imaging of cellular morphology and nuclear condensation. Thirdly, we show that the protective effect of nifedipine is achieved through the reduction of cytosolic calcium, mitochondrial calcium, and ROS generation. Overall, we outline a framework for quantitative analysis of mitochondrial calcium and ROS using 3D imaging in laser scanning confocal microscopy and the open-source image analysis platform ImageJ. The proposed pipeline was used to visualize mitochondrial calcium and ROS level in individual cells that provide an understanding of molecular targets. Our findings suggest that the therapeutic value of nifedipine may potentially be evaluated in the context of COVID-19 therapeutic trials.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

Spatial protein analysis in developing tissues: a sampling-based image processing approach.

Advances in fluorescence microscopy approaches have made it relatively easy to generate multi-dimensional image volumes and have highlighted the need for flexible image analysis tools for the extraction of quantitative information from such data. Here we demonstrate that by focusing on simplified feature-based nuclear segmentation and probabilistic cytoplasmic detection we can create a tool that is able to extract geometry-based information from diverse mammalian tissue images. Our open-source image analysis platform, called ‘SilentMark’, can cope with three-dimensional noisy images and with crowded fields of cells to quantify signal intensity in different cellular compartments. Additionally, it provides tissue geometry related information, which allows one to quantify protein distribution with respect to marked regions of interest. The lightweight SilentMark algorithms have the advantage of not requiring multiple processors, graphics cards or training datasets and can be run even with just several hundred megabytes of memory. This makes it possible to use the method as a Web application, effectively eliminating setup hurdles and compatibility issues with operating systems. We test this platform on mouse pre-implantation embryos, embryonic stem cell-derived embryoid bodies and mouse embryonic heart, and relate protein localization to tissue geometry.This article is part of a discussion meeting issue ‘Contemporary morphogenesis’.

Integration of the ImageJ Ecosystem in the KNIME Analytics Platform.

Open-source software tools are often used for analysis of scientific image data due to their flexibility and transparency in dealing with rapidly evolving imaging technologies. The complex nature of image analysis problems frequently requires many tools to be used in conjunction, including image processing and analysis, data processing, machine learning and deep learning, statistical analysis of the results, visualization, correlation to heterogeneous but related data, and more. However, the development, and therefore application, of these computational tools is impeded by a lack of integration across platforms. Integration of tools goes beyond convenience, as it is impractical for one tool to anticipate and accommodate the current and future needs of every user. This problem is emphasized in the field of bioimage analysis, where various rapidly emerging methods are quickly being adopted by researchers. ImageJ is a popular open-source image analysis platform, with contributions from a global community resulting in hundreds of specialized routines for a wide array of scientific tasks. ImageJ's strength lies in its accessibility and extensibility, allowing researchers to easily improve the software to solve their image analysis tasks. However, ImageJ is not designed for development of complex end-to-end image analysis workflows. Scientists are often forced to create highly specialized and hard-to-reproduce scripts to orchestrate individual software fragments and cover the entire life-cycle of an analysis of an image dataset. KNIME Analytics Platform, a user-friendly data integration, analysis, and exploration workflow system, was designed to handle huge amounts of heterogeneous data in a platform-agnostic, computing environment and has been successful in meeting complex end-to-end demands in several communities, such as cheminformatics and mass spectrometry. Similar needs within the bioimage analysis community led to the creation of the KNIME Image Processing extension which integrates ImageJ into KNIME Analytics Platform, enabling researchers to develop reproducible and scalable workflows, integrating a diverse range of analysis tools. Here we present how users and developers alike can leverage the ImageJ ecosystem via the KNIME Image Processing extension to provide robust and extensible image analysis within KNIME workflows. We illustrate the benefits of this integration with examples, as well as representative scientific use cases.

Defining host-pathogen interactions employing an artificial intelligence workflow.

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Imaging and Quantifying Mitochondrial Morphology: a Focus on the 3D Freeware MitoGraph

Mitochondria are found in a variety of shapes, from small round punctate structures to a highly‐interconnected web. This morphological diversity is important for function, but complicates quantification. Consequently, early quantification efforts relied on various qualitative descriptors that understandably reduce the complexity of the network leading to challenges in consistency across the field. Recent application of state‐of‐the‐art computational tools have resulted in more quantitative approaches. This work focuses on MitoGraph, an open‐source image analysis platform for measuring mitochondrial morphology initially optimized for use with Saccharomyces cerevisiae. MitoGraph was used to assess mitochondrial morphology of five different mammalian cells types, all of which were accurately segmented by MitoGraph analysis. MitoGraph also successfully differentiated between distinct mitochondrial morphologies that ranged from entirely fragmented to hyper‐elongated.Support or Funding InformationThis was work was supported by the National Institutes of Health grants: R01‐GM067180, R01‐ HL128240 and R41‐AG056253.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Methods for imaging mammalian mitochondrial morphology: A prospective on MitoGraph

Mitochondria are found in a variety of shapes, from small round punctate structures to a highly interconnected web. This morphological diversity is important for function, but complicates quantification. Consequently, early quantification efforts relied on various qualitative descriptors that understandably reduce the complexity of the network leading to challenges in consistency across the field. Recent application of state-of-the-art computational tools have resulted in more quantitative approaches. This prospective highlights the implementation of MitoGraph, an open-source image analysis platform for measuring mitochondrial morphology initially optimized for use with Saccharomyces cerevisiae. Here Mitograph was assessed on five different mammalian cells types, all of which were accurately segmented by MitoGraph analysis. MitoGraph also successfully differentiated between distinct mitochondrial morphologies that ranged from entirely fragmented to hyper-elongated. General recommendations are also provided for confocal imaging of labeled mitochondria (using mito-YFP, MitoTracker dyes and immunostaining parameters). Widespread adoption of MitoGraph will help achieve a long-sought goal of consistent and reproducible quantification of mitochondrial morphology.

SAMA: A Method for 3D Morphological Analysis.

Three-dimensional (3D) culture models are critical tools for understanding tissue morphogenesis. A key requirement for their analysis is the ability to reconstruct the tissue into computational models that allow quantitative evaluation of the formed structures. Here, we present Software for Automated Morphological Analysis (SAMA), a method by which epithelial structures grown in 3D cultures can be imaged, reconstructed and analyzed with minimum human intervention. SAMA allows quantitative analysis of key features of epithelial morphogenesis such as ductal elongation, branching and lumen formation that distinguish different hormonal treatments. SAMA is a user-friendly set of customized macros operated via FIJI (http://fiji.sc/Fiji), an open-source image analysis platform in combination with a set of functions in R (http://www.r-project.org/), an open-source program for statistical analysis. SAMA enables a rapid, exhaustive and quantitative 3D analysis of the shape of a population of structures in a 3D image. SAMA is cross-platform, licensed under the GPLv3 and available at http://montevil.theobio.org/content/sama.

Neuron J is a Rapid and Reliable Open Source Tool for Evaluating Corneal Nerve Density in Herpes Simplex Keratitis

In vivo confocal microscopy (IVCM) demonstrates reduction in corneal sub-basal nerve density in herpes simplex keratitis (HSK). Image J is an open source image-analysis platform that can be combined with a nerve tracer, Neuron J. We sought to compare the reliability and speed of corneal nerve density quantification between these modalities and their relation to clinical damage. A total of 16 eyes (14 patients) with chronic HSK was assessed clinically and by IVCM. Randomly ordered triplicate, representative images from the central cornea were presented to two masked observers and corneal sub-basal nerve density was measured using Image J/Neuron J. Agreement was quantified using intraclass correlation coefficients (ICC), Bland-Altman plots together with mean difference, and level of agreement (LoA). The median nerve density was measured at 7.1 mm/mm(2) (quartiles, 3.3-11.2), with Neuron-J demonstrating good intra-/interobserver agreement (ICC, 0.96-0.99; P < 0.001; mean difference, 0.1-1.4; LoA, <±3.3). Intraeye reliability was less consistent (mean difference, 1.7-2.3; LoA, ±8.8-9.8). Neuron J was highly comparable to Image J for both observers (ICC, 1.0; P < 0.001; mean difference, <0.2; LoA, ±<1.2) and significantly faster than Image J (median, 49 vs. 102 seconds, P < 0.001). Diminished nerve density was associated with corneal opacification and reduction in visual acuity (both P = 0.03). The IVCM combined with Neuron J affords objective, user-friendly, and fast quantification of corneal nerve damage in HSK. It provides semiobjective phenotyping of the sequelae of neurotrophic corneal damage and offers a potential tool for measuring vulnerability to relapse or additional infections. Further exploration in a larger longitudinal cohort is warranted.

Automated Detection and Analysis of Ca2+ Sparks in x-y Image Stacks Using a Thresholding Algorithm Implemented within the Open-Source Image Analysis Platform ImageJ

Previous studies have used analysis of Ca2+ sparks extensively to investigate both normal and pathological Ca2+ regulation in cardiac myocytes. The great majority of these studies used line-scan confocal imaging. In part, this is because the development of open-source software for automatic detection of Ca2+ sparks in line-scan images has greatly simplified data analysis. A disadvantage of line-scan imaging is that data are collected from a single row of pixels, representing only a small fraction of the cell, and in many instances x-y confocal imaging is preferable. However, the limited availability of software for Ca2+ spark analysis in two-dimensional x-y image stacks presents an obstacle to its wider application. This study describes the development and characterization of software to enable automatic detection and analysis of Ca2+ sparks within x-y image stacks, implemented as a plugin within the open-source image analysis platform ImageJ. The program includes methods to enable precise identification of cells within confocal fluorescence images, compensation for changes in background fluorescence, and options that allow exclusion of events based on spatial characteristics.

Automated Detection and Analysis of Ca2+ Sparks in X-Y Image Stacks using a Novel Algorithm Implemented Within the Open-Source Image Analysis Platform, Imagej

This study describes the development and characterisation of software to enable automatic detection and analysis of Ca2+ sparks within x-y image stacks, implemented as a plugin within the open source image analysis platform, ImageJ. The aim was to implement a "conventional" algorithm whereby sparks were identified by applying a threshold (θ) to the normalised (F/F0) image: θ = background fluorescence within the cell + SD ∗ ‘ε’, a user defined variable. A 2 stage interactive method with a graphical user interface (GUI) was used to ensure precise identification of the cell boundary and creation of a binary cell mask, which is subsequently used to exclude all regions outside the cell. The algorithm separates spark detection and analysis, allowing image processing to be applied independently at both stages. Filters also allow exclusion of events based on spark width or morphology. Novel methods are included to allow correction of time dependent changes in background fluorescence (e.g. due to bleaching), which would otherwise compromise spark detection by thresholding. The main outputs (amplitude, width, duration and spark mass) are presented in tabular form. In addition, an interactive GUI allows each spark to be examined, along with its measurements, and the associated Gaussian curve fit. A "Kill" button allows obvious errors in detection to be excluded. The performance of the algorithm was tested both on synthesised images (values of ε ranging from 3.0-4.2 and signal to noise ratios of 2, 3 or 4) and on x-y confocal fluorescence images from fluo-3 loaded rat ventricular myocytes. In both cases the performance was comparable to that reported previously for threshold based detection methods applied to line-scan images.