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Growth and morphogenesis in plants depend on cell wall mechanics and on turgor pressure. Nanoindentation methods, such as atomic force microscopy (AFM), enable measurements of mechanical properties of a tissue at subcellular resolution, while confocal microscopy of tissues expressing fluorescent reporters indicates cell identity. Associating mechanical data with specific cells is essential to reveal the links between cell identity and cell mechanics. Here we describe an image analysis protocol that allows us to segment AFM scans containing information on tissue topography and/or mechanics, to stitch several scans in order to reconstitute an entire region of the tissue investigated, to segment the scans and label cells, and to associate labeled cells to the projection of confocal images. Thus all mechanical data can be mapped to the corresponding cells and to their identity. This protocol is implemented using NanoIndentation, a plugin that we are developing in the Fiji distribution of ImageJ.

Microglial cells are the first line of defense within the central nervous system, with morphological characterization being widely used to define their activation status. Most methods to evaluate microglia status are manual, and, therefore, often biased, inaccurate, and time consuming. In fact, the process to collect morphological data starts with the acquisition of photomicrographs from where images of single cells are extracted. Then, the researcher collects the morphological features that characterize each cell. However, a manual data collection process from single cells can take weeks to complete. This work describes an open-source ImageJ plugin, MorphData, which automatizes the data extraction process of morphological features of single microglial cells. The plugin collects, processes, and organizes features associated with cell complexity and ramification. In a computer with limited computing power, MorphData was able to handle 699 single cells in less than 14 minutes. The same process, if performed manually, would take almost 19 working days. Overall, MorphData significantly reduces the time taken to collect morphological data from microglial cells, which can then be used to study, understand, and characterize microglia behavior in the brain of human patients or of animal models of neurological and psychiatric diseases.

Background Motor and non-motor behavior analyses are increasingly utilizied in drug discovery and screening, detection of neurobehavioral disorders and chemical toxicology. The emergence of computational approaches has helped to develop different tools to analyse complex behaviors. Analysis of locomotor behavior helps in understanding the motor neuron disorders like Parkinson’s Disease. Although many animal models are available to study the locomotion, adult zebrafish has emerged as a simple and efficient model to study this behavior. An inexpensive and easily customizable tool is required to replace the licensed and expensive set-up to analyse the locomotor behavior. New method In this study we have optimized the ImageJ plugin wrMTrck to analyse motor and non-motor behaviors in adult zebrafish. We have generated a macro to simplify the preprocessing and tracking. Subsequently, we have developed a data analysis sheet to analyse various behavioral end points. Results We have successfully developed an inexpensive video acquisition set-up and optimized wrMTrck for adult zebrafish. In order to demonstrate the efficacy of this method, adult zebrafish were injected with MPTP and motor and non-motor behaviors were analysed. Expectedly, MPTP injected fish showed decrease in dopamine level and dat expression level, which subsequently led to locomotor behavioral defects as well as anxiety, a non-motor symptom of PD. Comparison with existing method(s) Further, the obtained results were validated by another ImageJ macro developed by Pelkowski et al. (2011) and we observed identical trajectories. Conclusions The usefulness of popular ImageJ plugin wrMTrck and this extended protocol will be helpful to quantify motor and non-motor behavioral parameters.

When confined, cells have recently been shown to undergo a phenotypic switch to what has been termed, fast amoeboid (leader bleb-based) migration. However, as this is a nascent area of research, few tools are available for the rapid analysis of cell behavior. Here, we demonstrate that a novel Fiji/ImageJ-based plugin, Analyze_Blebs, can be used to quickly obtain cell migration parameters and morphometrics from time lapse images. As validation, we show that Analyze_Blebs can detect significant differences in cell migration and morphometrics, such as the largest bleb size, upon introducing different live markers of F-actin, including F-tractin and LifeAct tagged with green and red fluorescent proteins. We also demonstrate, using flow cytometry, that live markers increase total levels of F-actin. Furthermore, that F-tractin increases cell stiffness, which was found to correlate with a decrease in migration, thus reaffirming the importance of cell mechanics as a determinant of Leader Bleb-Based Migration (LBBM).Highlight summaryA new plugin, Analyze_Blebs, enables the rapid analysis of cell migration and morphometrics of fast amoeboid cells. Morphometrics combined with cell stiffness are found to be predictive of confined, fast amoeboid (leader bleb-based), migration.

Skeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

AnnotatorJ combines single-cell identification with deep learning (DL) and manual annotation. Cellular analysis quality depends on accurate and reliable detection and segmentation of cells so that the subsequent steps of analyses, for example, expression measurements, may be carried out precisely and without bias. DL has recently become a popular way of segmenting cells, performing unimaginably better than conventional methods. However, such DL applications may be trained on a large amount of annotated data to be able to match the highest expectations. High-quality annotations are unfortunately expensive as they require field experts to create them, and often cannot be shared outside the lab due to medical regulations. We propose AnnotatorJ, an ImageJ plugin for the semiautomatic annotation of cells (or generally, objects of interest) on (not only) microscopy images in 2D that helps find the true contour of individual objects by applying U-Net-based presegmentation. The manual labor of hand annotating cells can be significantly accelerated by using our tool. Thus, it enables users to create such datasets that could potentially increase the accuracy of state-of-the-art solutions, DL or otherwise, when used as training data.

INTRODUCTION Real-Time Optical Vascular Imaging (RTOVI) is a technology developed in the Centre for Oral Clinical & Translational Sciences, within the Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, that allows rapid and preparation free, in vivo imaging of the microvascular anatomy of the human oral cavity. Microvascular changes are known to be related to disease subtypes, in particular cancer. This makes in vivo microvascular examination clinically valuable. However, at present there is lack of any analytical method able to objectively assess microvascular morphology images. DISCUSSION The assessment of microvascular morphology based on a subjective evaluation was proven to be unreliable. There was a need to develop a software-based analysis for in vivo microvascular images to support the validation of RTOVI. This paper reviews the authors work to develop and test an automated microvascular analysis method for RTOVI based on ImageJ, an open-source software. This allowed to determined which parameters offered a more robust mathematical representation of the microvascular anatomy of the gingival margin, such as the mean area per capillary and mean aspect ratio. However, in vivo microvascular images from elsewhere within the oral cavity posed a bigger challenge to the analysis procedure due to the microvascular architectural complexity and poorer contrast. Angiogenesis Analyzer, a well-known ImageJ plugin used for the quantification of in vitro microvascular images, is under development in collaboration with the University of Paris Est Creteil. The aim of this work is to obtain an automated analysis method for in vivo microvascular images able to offer a solid foundation for the diagnostic potential of RTOVI and subsequent clinical integration of this technology. CONCLUSION An automated analysis method for in vivo microvascular images is paramount before any attempt to clinically validate RTOVI. Our initial work of testing a software-based analysis demonstrated the effectiveness of some parameters, which is valuable for future work, and led us to move into a more sophisticated method involving customising the Angiogenesis Analyzer plugin. This is an essential step, aiming to extend the potential of in vivo microscopy with the clinical integration of RTOVI. LAY DESCRIPTION This article summarises the initial research work done in the field on in vivo microvascular imaging aiming to develop a technique for the diagnosis of oral diseases based on the shape of small blood vessels found just below the surface of the "skin" inside the mouth. This offers the potential to examine lesions without the need to take a sample (biopsy/cutting tissue) to observe it microscopically. This ultimately offers a potential to accelerate diagnostic decision making, avoid unpleasant and often deterrent surgical procedures and reducing diagnostic laboratory time and cost burdens. However, in order to assess images of small blood vessels obtained in clinic, we needed to develop and test a software-based analysis to avoid the subjective human interpretation, known not to work. This article describes the authors journey to achieve an automated and sophisticated analysis method unique in the world for in vivo microvascular images derived from real-time optical vascular imaging.

Morphometry characterization is an important procedure in describing neuronal cultures and identifying phenotypic differences. This task usually requires labor-intensive measurements and the classification of numerous neurites from large numbers of neurons in culture. To automate these measurements, we wrote AutoNeuriteJ, an imageJ/Fiji plugin that measures and classifies neurites from a very large number of neurons. We showed that AutoNeuriteJ is able to detect variations of neuritic growth induced by several compounds known to affect the neuronal growth. In these experiments measurement of more than 5000 mouse neurons per conditions was obtained within a few hours. Moreover, by analyzing mouse neurons deficient for the microtubule associated protein 6 (MAP6) and wild type neurons we illustrate that AutoNeuriteJ is capable to detect subtle phenotypic difference in axonal length. Overall the use of AutoNeuriteJ will provide rapid, unbiased and accurate measurement of neuron morphologies.

We present an open-source implementation of the fluctuation-based nanoscopy method MUSICAL for ImageJ. This implementation improves the algorithm’s computational efficiency and takes advantage of multi-threading to provide orders of magnitude faster reconstructions than the original MATLAB implementation. In addition, the plugin is capable of generating super-resolution videos from large stacks of time-lapse images via an interleaved reconstruction, thus enabling easy-to-use multi-color super-resolution imaging of dynamic systems.

Tortuosity is an important geometrical parameter of the pore or grain network in a porous medium. Here we present and discuss an implementation of a plugin to estimate the pore/grain network tortuosity of a porous medium sample. The tortuosity is estimated according to the geometric reconstruction algorithm that can be applied to 2D or 3D μCT image samples. To illustrate the tortuosity plugin features, applications are given i) for the 2D case: three synthetic image samples being one horizontal straight line, one diagonal straight line and a curved line, and additionally it is considered a Dolomite Silurian rock sample, ii) for the 3D case: the pore network of a Berea and Fontainebleau sandstone and a Grosmont carbonte reservoir core samples; the grain network of a distal-radius trabecular bone sample and, finally, the pore network of a Rhodic Ferralsol soil sample. Some comments and discussions about the tortuosity estimated with the current algorithm are also addressed.

Background Phosphorylated histone H2AX, also known as gamma H2AX, forms mu m-sized nuclear foci at the sites of DNA double-strand breaks (DSBs) induced by ionizing radiation and other agents. Due to their specificity and sensitivity, gamma H2AX immunoassays have become the gold standard for studying DSB induction and repair. One of these assays relies on the immunofluorescent staining of gamma H2AX followed by microscopic imaging and foci counting. During the last years, semi- and fully automated image analysis, capable of fast detection and quantification of gamma H2AX foci in large datasets of fluorescence images, are gradually replacing the traditional method of manual foci counting. A major drawback of the non-commercial software for foci counting (available so far) is that they are restricted to 2D-image data. In practice, these algorithms are useful for counting the foci located close to the midsection plane of the nucleus, while the out-of-plane foci are neglected. Results To overcome the limitations of 2D foci counting, we present a freely available ImageJ-based plugin (FocAn) for automated 3D analysis of gamma H2AX foci in z-image stacks acquired by confocal fluorescence microscopy. The image-stack processing algorithm implemented in FocAn is capable of automatic 3D recognition of individual cell nuclei and gamma H2AX foci, as well as evaluation of the total foci number per cell nucleus. The FocAn algorithm consists of two parts: nucleus identification and foci detection, each employing specific sequences of auto local thresholding in combination with watershed segmentation techniques. We validated the FocAn algorithm using fluorescence-labeled gamma H2AX in two glioblastoma cell lines, irradiated with 2 Gy and given up to 24 h post-irradiation for repair. We found that the data obtained with FocAn agreed well with those obtained with an already available software (FoCo) and manual counting. Moreover, FocAn was capable of identifying overlapping foci in 3D space, which ensured accurate foci counting even at high DSB density of up to similar to 200 DSB/nucleus. Conclusions FocAn is freely available an open-source 3D foci analyzer. The user-friendly algorithm FocAn requires little supervision and can automatically count the amount of DNA-DSBs, i.e. fluorescence-labeled gamma H2AX foci, in 3D image stacks acquired by laser-scanning microscopes without additional nuclei staining.

Cashew is a high-value and the third largest edible tree nut that is universally consumed as a snack or included in food preparations. Classification of whole and split cashews in industries is carried out manually by visual inspection and hand-picking, which is time-consuming, tedious, challenging, and laborious. A machine vision methodology of capturing cashew shadows and associated image processing ImageJ plugin to classify cashews into whole and splits were developed and tested. The developed classification algorithm was based on a novel idea of using surface grayscale-intensity-profile for split-up cashews and object shadows for split-down and whole cashews. Out of several features derived from grayscale-intensity-profile values, the “length of curve” best classified the split-up cashews from others. The challenge of classifying the whole and split-down cashews was addressed by a new “shadow to total-area ratio.” An accuracy of 100 % was achieved by the algorithm. A process scale-up by increasing the height and power of light source was also proposed. The promising results suggest that the developed algorithm can be coupled with a suitable hardware system to perform accurate classification of the whole and split cashews.

Noninvasive three- and four-dimensional X-ray imaging approaches have proved to be valuable analysis tools for vadose zone research. One of the main bottlenecks for applying X-ray imaging to data sets with a large number of soil samples is the relatively large amount of time and expertise needed to extract quantitative data from the respective images. SoilJ is a plugin for the free and open imaging software ImageJ that aims at automating the corresponding processing steps for cylindrical soil columns. It includes modules for automatic column outline recognition, correction of image intensity bias, image segmentation, extraction of particulate organic matter and roots, soil surface topography detection, as well as morphology and percolation analyses. In this study, the functionality and precision of some key SoilJ features were demonstrated on five different image data sets of soils. SoilJ has proved to be useful for strongly decreasing the amount of time required for image processing of large image data sets. At the same time, it allows researchers with little experience in image processing to make use of X-ray imaging methods. The SoilJ source code is freely available and may be modified and extended at will by its users. It is intended to stimulate further community-driven development of this software.

Author(s): Love, Natalie L Rossington; Park, Isaac W; Mazer, Susan J | Abstract: PremiseHerbarium specimens have been used to detect climate-induced shifts in flowering time by using the day of year of collection (DOY) as a proxy for first or peak flowering date. Variation among herbarium sheets in their phenological status, however, undermines the assumption that DOY accurately represents any particular phenophase. Ignoring this variation can reduce the explanatory power of pheno-climatic models (PCMs) designed to predict the effects of climate on flowering date.MethodsHere we present a protocol for the phenological scoring of imaged herbarium specimens using an ImageJ plugin, and we introduce a quantitative metric of a specimen's phenological status, the phenological index (PI), which we use in PCMs to control for phenological variation among specimens of Streptanthus tortuosus (Brassicaceeae) when testing for the effects of climate on DOY. We demonstrate that including PI as an independent variable improves model fit.ResultsIncluding PI in PCMs increased the model R 2 relative to PCMs that excluded PI; regression coefficients for climatic parameters, however, remained constant.DiscussionOur protocol provides a simple, quantitative phenological metric for any observed plant. Including PI in PCMs increases R 2 and enables predictions of the DOY of any phenophase under any specified climatic conditions.

Image analysis is an important tool for several application fields, like biology and especially botany. Analysis of seed fossils can provide important information about their evolution, on agriculture origin, on plants domestication and knowledge of diets in ancient times. The aim of this work is to make the analysis process simple for biologists, by obtaining all the features needed for botanist user through a unique framework, that is still not available at the moment. We propose an ImageJ plugin able to extract morphological, textural and color features from seeds images in order to use them for classification. The experimental results have confirmed the goodness and correctness of the extracted features, making the proposed framework easily extendable to other application domains.

Microscopic images are widely used in plant biology as an essential source of information on morphometric characteristics of the cells and the topological characteristics of cellular tissue pattern due to modern computer vision algorithms. High-resolution 3D confocal images allow extracting quantitative characteristics describing the cell structure of leaf epidermis. For some issues in the study of cereal leaves development, it is required to apply the staining techniques with fluorescent dyes and to scan rather large fragments consisting of several frames. We aimed to develop a tool for processing multi-frame multi-channel 3D images obtained from confocal laser scanning microscopy and taking into account the peculiarities of the cereal leaves staining. We elaborated an ImageJ-plugin LSM-W2 that allows extracting data on Leaf Surface Morphology from Laser Scanning Microscopy images. The plugin is a crucial link in a workflow for obtaining data on structural properties of leaf epidermis and morphological properties of epidermal cells. It allows converting large lsm-files (laser scanning microscopy) into segmented 2D/3D images or tables with data on cells and/or nuclei sizes. In the article, we also represent some case studies showing the plugin application for solving biological tasks. Namely the plugin is applied in the following cases: defining parameters of jigsaw-puzzle pattern for maize leaf epidermal cells, analysis of the pavement cells morphological parameters for the mature wheat leaf grown under control and water deficit conditions, initiation of cell longitudinal rows, and detection of guard mother cells emergence at the initial stages of the stomatal morphogenesis in the growth zone of a wheat leaf. The proposed plugin is efficient for high-throughput analysis of cellular architecture for cereal leaf epidermis. The workflow implies using inexpensive and rapid sample preparation and does not require the applying of transgenesis and reporter genetic structures expanding the range of species and varieties to study. Obtained characteristics of the cell structure and patterns further could act as a basis for the development and verification for spatial models of plant tissues formation mechanisms accounting for structural features of cereal leaves. The implementation of this workflow is available as an ImageJ plugin distributed as a part of the Fiji project (FijiisjustImageJ: https://fiji.sc/ ). The plugin is freely available at https://imagej.net/LSM_Worker , https://github.com/JmanJ/LSM_Worker and http://pixie.bionet.nsc.ru/LSM_WORKER/ .

An efficient and low-cost method of examining larval movement in Drosophila melanogaster is needed to study how mutations and/or alterations in the muscular, neural, and olfactory systems affect locomotor behavior. Here, we describe the implementation of wrMTrck, a freely available ImageJ plugin originally developed for examining multiple behavioral parameters in the nematode C. elegans. Our optimized method is rapid, reproducible and does not require automated microscope setups or the purchase of proprietary software. To demonstrate the utility of this method, we analyzed the velocity and crawling paths of two Drosophila mutants that affect muscle structure and/or function. Additionally, we show that this approach is useful for tracking the behavior of adult insects, including Tribolium castaneum and Drosophila melanogaster.

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.

Insight into the function and regulation of biological molecules can often be obtained by determining which cell structures and other molecules they localize with (i.e. colocalization). Here we describe an open source plugin for ImageJ called EzColocalization to visualize and measure colocalization in microscopy images. EzColocalization is designed to be easy to use and customize for researchers with minimal experience in quantitative microscopy and computer programming. Features of EzColocalization include: (i) tools to select individual cells and organisms from images; (ii) filters to select specific types of cells and organisms based on physical parameters and signal intensity; (iii) heat maps and scatterplots to visualize the localization patterns of reporters; (iv) multiple metrics to measure colocalization for two or three reporters; (v) metric matrices to systematically measure colocalization at multiple combinations of signal intensity thresholds; and (vi) data tables that provide detailed information on each cell in a sample. These features make EzColocalization well-suited for experiments with low reporter signal, complex patterns of localization, and heterogeneous populations of cells and organisms.

Recent advances in fast volumetric imaging have enabled rapid generation of large amounts of multi-dimensional functional data. While many computer frameworks exist for data storage and analysis of the multi-gigabyte Ca 2+ imaging experiments in neurons, they are less useful for analyzing Ca 2+ dynamics in astrocytes, where transients do not follow a predictable spatio-temporal distribution pattern. In this manuscript, we provide a detailed protocol and commentary for recording and analyzing three-dimensional (3D) Ca 2+ transients through time in GCaMP6f-expressing astrocytes of adult brain slices in response to axonal stimulation, using our recently developed tools to perform interactive exploration, filtering, and time-correlation analysis of the transients. In addition to the protocol, we release our in-house software tools and discuss parameters pertinent to conducting axonal stimulation/response experiments across various brain regions and conditions. Our software tools are available from the Volterra Lab webpage at https://wwwfbm.unil.ch/dnf/group/glia-an-active-synaptic-partner/member/volterra-andrea-volterra in the form of software plugins for Image J (NIH)-a de facto standard in scientific image analysis. Three programs are available: MultiROI_TZ_profiler for interactive graphing of several movable ROIs simultaneously, Gaussian_Filter5D for Gaussian filtering in several dimensions, and Correlation_Calculator for computing various cross-correlation parameters on voxel collections through time.

Micro-computed tomography (micro-CT) is routinely used to assess bone quantity and trabecular microstructural properties in small animals under different bone loss conditions. However, the standard approach for trabecular analysis of micro-CT images is slice-by-slice semi-automatic hand-contouring, which is labor intensive and error prone. Described here is an efficient method for automatic segmentation of trabecular bones according to the bone's outer boundaries, where trabecular bones can be identified and segmented automatically with accuracy with less operator bias when appropriate segmentation parameters are set. To profile satisfactory segmentation parameters, an image stack of segmentation results is displayed, where all possible combinations of the segmentation parameters are changed one by one in sequence, and segmentation results with associated parameters can easily be visually checked. As a quality-control feature of the plugin, simulated standard objects are quantified where the measured quantities can be compared with theoretical values. Layer-by-layer quantification of trabecular properties and trabecular thicknesses are reported by such a plugin, and the distributions of such properties within the selected regions can be profiled easily. Although layer-by-layer quantification retains more information about trabecular bones and facilitates further statistical analysis of structural changes, such measures are unavailable from the output of current commercial software, where only a single quantified value for each parameter is reported for each sample. Therefore, the described workflows are better approaches for analyzing trabecular bones with accuracy and efficiency.

High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.

The quantification of synapses is instrumental to measure the evolution of synaptic densities of neurons under the effect of some physiological conditions, neuronal diseases or even drug treatments. However, the manual quantification of synapses is a tedious, error-prone, time-consuming and subjective task; therefore, reliable tools that might automate this process are desirable. In this paper, we present SynapCountJ, an ImageJ plugin, that can measure synaptic density of individual neurons obtained by immunofluorescence techniques, and also can be applied for batch processing of neurons that have been obtained in the same experiment or using the same setting. The procedure to quantify synapses implemented in SynapCountJ is based on the colocalization of three images of the same neuron (the neuron marked with two antibody markers and the structure of the neuron) and is inspired by methods coming from Computational Algebraic Topology. SynapCountJ provides a procedure to semi-automatically quantify the number of synapses of neuron cultures; as a result, the time required for such an analysis is greatly reduced. The computations performed by SynapCountJ have been validated by comparing the results with those of a formally verified algorithm (implemented in a different system).