plugin

Description

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.

need a thumbnail
Description

MiNA is a simplified workflow for analyzing mitochondrial morphology using fluorescence images or 3D stacks in Fiji. The workflow makes use of ImageJ Ops3D ViewerSkeletonize (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.

example skeleton image (from https://imagej.net/plugins/mina#processing-pipeline-and-usage)
Description

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. 

has function
SynActJ workflow
Description

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.

Correlia
Description

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.

Empanada-napari