Free and open source

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.

This workflow applies a Stardist pre-trained model (versatile_fluo or versatile_HE) depending on the input images ie. uses both models for a dataset including both fluorescence (grayscale or RGB where all channels are equal) and H&E stained (RGB where channels are not equal) images.

This version uses tensorflow CPU version (See Dockerfile) to ensure compatibility with a larger number of computers. A GPU version should be possible by adapting the Dockerfile with tensorflow-gpu and/or nvidia-docker images.

This workflow processes a group of images containing cells with discernible nuclei and segments the nuclei and outputs a binary mask that show where nuclei were detected. It performs 2D nuclei segmentation using pre-trained nuclei segmentation models of Cellpose. And it was developed as a test workflow for Neubias BIAFLOWS Benchmarking tool.

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.

It stitches 3D tiles from terabyte-size microscopy datasets. Stitching does not require any prior information on the actual positions of the tiles, sample fiducials, or conversion of raw TIFF images, and the stitched images can be explored instantly.

MosaicExplorerJ was specifically designed to process lightsheet microscopy datasets from optically cleared samples. It can handle multiple fluorescence channels, dual-side lightsheet illumination and dual-side camera detection.