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.


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).

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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.


KNIME workflow to visualize a dataset described by multiple quantitative features (ex: a list of samples or cells, each described with multiple morphological features) as a 3D cloud of points (each point corresponding to one sample/cell) as well as a line plot (1 line per sample/cell).

For the 3D plot, the workflow uses Principal Component Analysis (PCA) for dimensionality reduction, ie it simplifies the information for each sample from n-features to 3 pseudo-features which are used as x,y,z-coordinates for each sample. The original features should cover similar value range, to make sure the PCA is not biased towards the large values features. One option is to normalize the values (min/max or Z-score). 

Also make sure that the resulting PCA represents a decent % of the original data variance (at least 70%). Otherwise the PCA plot will not be representative of the original data-distribution. The % is shown in the title of the PCA plot.

The workflow is interactive and so selecting in one panel of the figure will highlight in the other panel too.

It was originally published for the visualization of phenotypic kidney features in zebrafish, but the workflow is generic by design and can be reused for any quantitative feature set. 


The library contains several helper functions to generate MoBIE project folders and add data to it.  Itis a python library to generate data in the MoBIE data storage layout. 

For further information, look to

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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.