A Component is an implementation of certain image processing / analysis algorithms.

Each component alone does not solve a Bioimage Analysis problem.

These problems can be addressed by combining such components into workflows.


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


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.

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The napari-pyclesperanto-assistant is a yet experimental napari plugin for building GPU-accelerated image processing workflows targeting life-sciences and bio-image analysis. It is part of the clEsperanto project. It uses pyclesperanto and pyopencl as backend for processing images.

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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.

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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.


Image Analysis Training Resources

Submitted by Perrine on Wed, 06/30/2021 - 14:15

This is a resource for image analysis training material, with a focus on research in the life sciences.

Currently, this resource is mainly meant to serve image analysis trainers, helping them to design courses. However, we might add more text (or videos) to the material such that it could also be used by students for self-directed study.