Image segmentation

Image segmentation is (one of) the (few) concept(s) on the border between Image (pre)processing (Image->Image) and Image analysis (Image->Data).


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.

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Relate is a correlative software package optimised to work with EM, EDS, EBSD, & AFM data and images.  It provides the tools you need to correlate data from different microscopes, visualise multi-layered data in 2D and 3D, and conduct correlative analyses.

  • Combining data from different imaging modalities (e.g. AFM, EDS & EBSD)

  • Interactive display of multi-layer correlated data

  • Analytical tools for metadata interrogation

  • Documented workflows and processes


  • Import data from AZtec using the H5oina file format
  • Import AFM data
  • Correlate both sets of data using intuitive image overlays and image matching tools
  • Produce combined multimodal datasets


  • 2D display of multi-layered data
  • 3D visualisation of topography combined with AFM material properties, EM images, and EDS & EBSD map overlays
  • Customisation of colour palettes, data overlays, image rendering options, and document display
  • Export images and animations


  • Generate profile (cross section) views of multimodal data
  • Measure and quantify data across multiple layers
  • Analyse areas via data thresholding using amount of x-ray counts, phase maps, height, or other material properties.
  • Select an extensive range of measurement parameters
  • Export analytical data to text or CSV files
Relate analysis workflow example

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.


Machine Learning made easy

APEER ML provides an easy way to train your own machine learning
models and segment your microscopy images. No expertise or coding required.

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