plugin

Fiji plugin to segment oocyte and zona pellucida contours from transmitted light images and extract hundreds of morphological features to describe numerically the oocyte. Segmentation is based on trained neural networks (U-Net) that were trained on both mouse and human oocytes (in prophase and meiosis I) acquired in different conditions. They are freely avaialable on the github repository and can be retrained if necessary. Oocytor also have options to extract hundreds of morphological/intensity features to characterize manually the oocyte (eg perimeter, texture...). These features can also be used in machine learning pipeline for automatic phenotyping.

The BioVoxxel Toolbox is a suite which contains plugins and some macros dealing with image filtering, image segmentation and binary image processing and analysis. The following plugins are hosted here:

• Extended Particle Analyzer
• Binary Feature Extractor
• Speckle Inspector
• Watershed Irregular Features
• EDM Binary Operations
• Filter Check
• Pseudo flat-field correction
• Convoluted Background Subtraction
• 2D Particle Distribution (Distribution_Analysis)
• Cluster Indicator
• SSIDC Cluster Indicator
• Gaussian weighted Median filter
• Adaptive Filter
• Enhance True Color Contrast
• Mode and Differential Limited Mean Binarization
• Basic Recursive Filter

Fast4DReg is a Fiji macro for drift correction for 2D and 3D video and is able to correct drift in all x-, y- and/or z-directions. Fast4DReg creates intensity projections along both axes and estimates their drift using cross-correlation based drift correction, and then translates the video frame by frame. Additionally, Fast4DReg can be used for alignment multi-channel 2D or 3D images which is particularly useful for instruments that suffer from a misalignment of channels.

TissUUmaps is a browser-based tool for fast visualization and exploration of millions of data points overlaying a tissue sample. TissUUmaps can be used as a web service or locally in your computer, and allows users to share regions of interest and local statistics.

MATLAB app to characterize nanoparticles imaged with super-resolution microscopy. nanoFeatures will read text and csv files from the NIKON and ONI microscopes and from the ThunderSTORM Fiji plugin, then cluster the localizations and filter by size and sphericity and finally output nanoparticle features like size, aspect ratio, and number of localizations per cluster (total and for each channel).

These are commands that create or process binary (black and white) images. Typical morphological operations/functions can be found here.

ELEPHANT is a platform for 3D cell tracking, based on incremental and interactive deep learning.
It implements a client-server architecture. The server is built as a web application that serves deep learning-based algorithms. The client application is implemented by extending Mastodon, providing a user interface for annotation, proofreading and visualization.

JIPipe is a visual programming language to realize code-free workflow building for ImageJ-based image analyses. GUI, graphical user interface. Currently, JIPipe unifies the functionality of over 1,000 ImageJ commands into a standardized interface, represented as nodes in the pipeline flow chart. The window-based data management implemented in ImageJ is replaced with a table-based model designed for batch processing. JIPipe is also available from within the ImageJ update service.

Fractal is a framework to process high-content imaging data at scale and prepare it for interactive visualization. Fractal provides distributed workflows that convert TBs of image data into OME-Zarr files. The platform then processes the 3D image data by applying tasks like illumination correction, maximum intensity projection, 3D segmentation using cellpose and measurements using napari workflows. The pyramidal OME-Zarr files enable interactive visualization in the napari viewer.

MiNA is a simplified workflow for analyzing mitochondrial morphology using fluorescence images or 3D stacks in Fiji. The workflow makes use of ImageJ Ops, 3D Viewer, Skeletonize (2D/3D), Analyze Skeleton, and Ridge Detection. In short, the tool estimates mitochondrial footprint (or volume) from a binarized copy of the image as well as the lengths of mitochondrial structures using a topological skeleton. The values are reported in a table and overlays (or a 3D rendering) are generated to assess the accuracy of the analysis.

SynActJ (Synaptic Activity in ImageJ) is an easy-to-use fully open-source workflow that enables automated image and data analysis of synaptic activity. The workflow consists of a Fiji plugin performing the automated image analysis of active synapses in time-lapse movies via an interactive seeded watershed segmentation that can be easily adjusted and applied to a dataset in batch mode. The extracted intensity traces of each synaptic bouton are automatically processed, analyzed, and plotted using an R Shiny workflow. 

Correlia is an open-source ImageJ/FIJI plug-in for the registration of 2D multi-modal microscopy data-sets. The software is developed at ProVIS - Centre for Correlative Microscopy and is specifically designed for the needs of chemical microscopy involving various micrographs as well as chemical maps at different resolutions and field-of-views.

The empanada-napari plugin is built to democratize deep learning image segmentation for researchers in electron microscopy (EM). It ships with MitoNet, a generalist model for the instance segmentation of mitochondria. There are also tools to quickly build and annotate training datasets, train generic panoptic segmentation models, finetune existing models, and scalably run inference on 2D or 3D data. To make segmentation model training faster and more robust, CEM pre-trained weights are used by default. These weights were trained using an unsupervised learning algorithm on over 1.5 million EM images from hundreds of unique EM datasets making them remarkably general.

ModularImageAnalysis (MIA) is an ImageJ plugin which provides a modular framework for assembling image and object analysis workflows. Detected objects can be transformed, filtered, measured and related. Analysis workflows are batch-enabled by default, allowing easy processing of high-content datasets.

MIA is designed for “out-of-the-box” compatibility with spatially-calibrated 5D images, yielding measurements in both pixel and physical units.  Functionality can be extended both internally, via integration with SciJava’s scripting interface, and externally, with Java modules that extend the MIA framework. Both have full access to all objects and images in the analysis workspace.

Workflows are, by default, compatible with batch processing multiple files within a single folder. Thanks to Bio-Formats, MIA has native support for multi-series image formats such as Leica .lif and Nikon .nd2.

Workflows can be automated from initial image loading through processing, object detection, measurement extraction, visualisation, and data exporting. MIA includes near 200 modules integrated with key ImageJ plugins such as Bio-Formats, TrackMate and Weka Trainable Segmentation.

Module(s) can be turned on/off dynamically in response to factors such as availability of images and objects, user inputs and measurement-based filters. Switches can also be added to “processing view” for easy workflow control.

MIA is developed in the Wolfson Bioimaging Facility at the University of Bristol.

BaSiC is a software tool for Background and Shading correction of Optical Microscopy Images. It implements an image correction method based on low-rank and sparse decomposition to solve both shading in space and background variation in time. It can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC is available as a Fiji/ImageJ plugin.

Removal of heterogeneous background from image data of single-molecule localization microscopy, using extreme value-based emitter recovery (EVER).

Quote:

EVER requires no manual adjustment of parameters and has been implemented as an easy-to-use ImageJ plugin that can immediately enhance the quality of reconstructed super-resolution images. This method is validated as an efficient way for robust nanoscale imaging of samples with heterogeneous background fluorescence, such as thicker tissue and cells.

AnnotatorJ is a Fiji Plugin to ease annotation of images, particulrly useful for Deep Learning or to validate an alogorithm. Interestingly, it allows annotation for instance segmentation, semantic segmentation, or bounding box annotations. It includes toolssuch as active contours to ease these annotations.

The tool exports rectangular regions, defined with the NDP.view 2 software (hammatsu) from the highest resolution version of the ndpi-images and saves them as tif-files.

Click the button and select the input folder. The input folder must contain pairs of ndpi and ndpa files. The regions will be exported to a subfolder of the input folder names zones.

This Fiji plugin is a python script for CLEM registration using deep learning, but it could be applied in principle to other modalities. The pretrained model was learned on chromatin SEM images and fluorescent staining, but a script is also provided to train an new model, based on CSBDeep. The registration is then performed as a feature based registration, using register virtual stack plugin (which extract features and then perform RANSAc. Editing the script in python gives access to more option (such as the transformation model to be used, similarity by default. Images need to be prepared such that they contain only one channel, but channel of ineterst (to be transformed with the same transformation) can be given as input, and Transform Virtual Stack plugin can be used as well.

This tool allows to analyze morphological characteristics of complex roots. While for young roots the root system architecture can be analyzed automatically, this is often not possible for more developed roots. The tool is inspired by the Sholl analysis used in neuronal studies. The tool creates a binary mask and the Euclidean Distance Transform from the input image. It then allows to draw concentric circles around a base point and to extract measures on or within the circles. Instead of circles, which present the distance from the base point, horizontal lines can be used, which present the distance in the soil from the base-line. The following features are currently implemented:

• The area of the root per distance/depth.
• The number of border pixel per distance/depth, giving an idea of the surface in contact with the soil.
• The maximum radius per distance/depth of a root, measured at the crossing points with the circles or lines.
• The number of crossings of roots with the circles or lines.
• The maximum distance to the left and the right from the vertical axis at crossing points with the circles or lines.

Local Z Projector is an ImageJ2 plugin, available in Fiji, that can perform local-Z projection of a 3D stack, possibly over time, possibly very large.

LZP performs projection of a surface of interest on a 2D plane from a 3D image. It is a simple tool that focuses on usability and is designed to be adaptable to many different use cases and image quality.

• It can work with 3D movies over time with multiple channels.
• It can work with images much larger than available RAM out of the box.
• It takes advantage of computers with multiple cores, and can be used in scripts.

MSER based on implementation in imglib2 provided as an interactive GUI tool for spot detection in 2/3/4D images.

Quote:

LaRoME = Label + Region Of Interest + Measure

Label image (aka Count Masks): An image in which pixels of an object have all the same value. Each object has a unique value.

Measurement image: An image in which pixels of an object have all the same value, corresponding to a measurement (Area, Angle, Mean...)

Using a Hamamatsu slide scanner such as the NanoZoomer, you may end up with NDPI files that can't always be directly open in standard image analysis software such as ImageJ. NDPITools is a collection of software that can convert NDPI files to standard TIFF files, possibly cutting them into smaller JPEG or TIFF pieces that will better fit into your computer's memory. It comes with a bundle of plugins for ImageJ which enable the use of the software directly inside ImageJ with point-and-click.

This small plugin demonstrates the use of OpenSlide in java: it  will extract an imageJ roi drawn from the thumbnail of the whole slide image, or the full image at the desired resolution from an hammatsu NDPI file. Note that z stack are not supported by openslide (neitheir ndpiS).

Set of Fiji plugins facilitating the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets.

The plugins can be installed by activating the Qualitative annotations update site in Fiji.

Analyze the clustering behavior of nuclei in 3D images. The centers of the nuclei are detected. The nuclei are filtered by the presence of a signal in a different channel. The clustering is done with the density based algorithm DBSCAN. The nearest neighbor distances between all nuclei and those outside and inside of the clusters are calculated.

MoBIE (Multimodal Big Image Data Exploration) is a framework for sharing and interactive browsing of multimodal big image data. The MoBIE Fiji viewer is based on BigDataViewer and enables browsing of MoBIE datasets. 

It is also called Platybrowser, and uses the n5 format.

This macro toolset offers additional click tools for the rapid annotations of ROI in ImageJ/Fiji.

The ROI 1-click tools can be setup with a predefined shape, and custom actions to perform upon click (Add to ROI Manager, Run Measure, Go to next slice, run a macro command...)

To install in Fiji, just activate the ROI 1-click tools 

The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. Its graphical user interface is largely inspired from the open source software "ImageJ". ImageM can also be run on the open source alternative software to Matlab, Octave.

ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

This is the ImageJ/Fiji plugin for StarDist, a cell/nuclei detection method for microscopy images with star-convex shape priors ( typically for Dapi like staining of nuclei). The plugin can be used to apply already trained models to new images.

Summary

Deep learning-based segmentation of cells, both fluorescence, and bright-field images ("a generalist algorithm for cellular segmentation"). The tool can be used either online or local or via notebooks (e.g. ZeroCostDL4Mic).

How to use it

cellpose can be used online via ready-to-use Jyupyter notebooks with very good documentation. These notebooks are listed here.

Local Installation

The general local installation procedure can be found here.

... Installing to Silicon Mac (M1 processor) needs some tricks, and as of October 2021, the following sequence of commands works. numba should be conda-installed before pip-installing the cellpose.

conda create --name cellpose python=3.8
conda activate cellpose
conda install numba
git clone https://github.com/MouseLand/cellpose.git
cd cellpose
pip install -e .

DeepImageJ is a user-friendly plugin that enables the use of a variety of pre-trained deep learning models in ImageJ and Fiji. The plugin bridges the gap between deep learning and standard life-science applications. DeepImageJ runs image-to-image operations on a standard CPU-based computer and does not require any deep learning expertise.

Training developper constructs and upload trained model, and made them available to users.

Models are available in a repository here.

It is macro recordable. It is advised to use DeepImageJ on a computer with GPU (CPU will likely be 20x slower)

The Morphonet Python API provide an easy interface to interact directly with the MorphoNet server. Very useful to upload, download your dataset and superimpose on it any quantitative and quantitative informations.

Vaa3d BJUT Fast Marching Spanning Tree algorithm dockerised workflow for BIAFLOWS

This plugin computes for each image element (pixel/voxel) the eigenvalues of the Hessian, which can be used for example to discriminate locally between plate-like, line-like, and blob-like image structures

This workflow detects spots in a 2D image by filtering the image by Laplacian of Gaussian (user defined radius), thresholding (user defined threshold) and finding local intensity maxima in mask distance map (Dmap).

Starting from image stacks, the nuclear boundary as well as nuclear bodies are segmented. As output, NucleusJ automatically measures 15 parameters quantifying shape and size of nuclei as well as intra-nuclear objects and the positioning of the objects within the nuclear volume.

This plugin converts all occurrences of red in a red/green image with magenta, effectively replacing it with a magenta/green merge.

Note: the plugin completely ignores the blue channel, and replaces it with a copy of the red channel.

This plugin ships automated methods for extracting trajectories of multiples objects in a sequence of 2D or 3D images. Up to version 2 it was known as the ‘Probabilistic particle tracker’ plugin.

LBADSA is based on the fitting of the Young-Laplace equation to the image data to measure drops.

DropSnake is based on B-spline snakes (active contours) to shape and measure a drop.

Smooth 2D Manifold Extraction (SME).

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

Protein array is used to analyze protein expressions by screening simultaneously several protein-molecule interactions such as protein-protein and protein-DNA interactions. In most cases, the detection of interactions leads to an image containing numerous lines of spots that will be analyzed by comparing tables of intensity values. To describe the observed different patterns of expression, users generally show histograms with the original associated images [1]. The “Protein Array Analyzer” gives a friendly way to exploit this type of analysis, thus allowing quantification, image modeling and comparative analysis of patterns.

The Protein Array Analyzer, which was programmed in ImageJ’s macro language, is an extention of the Dot Blot Analyzer, [2], [3] a graphically interfaced tool that greatly simplifying analysis of dot arrays.

Multi-template matching can be used to localize multiple objects using one or a set of template images.

Contrary to previous implementations that allow to use only one template, here a set of templates can be used or the initial template(s) can be transformed by rotation/flipping.

Multiple objects detection without redundant detections is possible thanks to a Non-Maxima Supression relying on the degree of overlap between detections.

The solution is available as a Fiji plugin (Multi-Template Matching AND IJ-OpenCV update sites), as a Python package (Multi-Template-Matching on PyPI) and as a KNIME workflow (via KNIME Hub).

Fiji plugin for detecting, tracking and quantifying filopodia

NEUBIAS-WG5 workflow for nuclei segmentation using ilastik v1.3.2 and Python post-processing.

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

NEUBIAS-WG5 workflow for nuclei segmentation using Mask-RCNN. The workflow uses Matterport Mask-RCNN. Keras implementation. The model was trained with Kaggle 2018 Data Science Bowl images.