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.




DeepCell is neural network library for single cell analysis, written in Python and built using TensorFlow and Keras.

DeepCell aids in biological analysis by automatically segmenting and classifying cells in optical microscopy images. This framework consumes raw images and provides uniquely annotated files as an output.

The jupyter session in the read docs are broken, but the one from the GitHub are functional (see usage example )




Web based viewer developped for google for very big data: 

Neuroglancer is a WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). The segmentation has to be done before loading the dataset, it is not done Inside the viewer.

This is not an official Google product.

It has among other the nice feature of beeing able to generate url for sharing a specific view.

Note that the only supported browser for now are 

  • Chrome >= 51
  • Firefox >= 46



Multi-Template matching


Multi-template matching can be used to localize multiple objects using one or a set of template images.

Contrary to previous implementations that allow to use only one template, here a set of templates can be used or the initial template(s) can be transformed by rotation/flipping.

Multiple objects detection without redundant detections is possible thanks to a Non-Maxima Supression relying on the degree of overlap between detections.

The solution is available as a Fiji plugin (Multi-Template Matching update site) and as a Python package (Multi-Template-Matching on PyPI)

has function
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Visualization of 3D images with Matlab

Bioimage Analyst

In this session we will use a 3D multichannel reconstruction of zebrafish larva to explore the visualization capabilities of Matlab. We will start from extracting and inspecting single slices and will continue with combining multiple channels, finally generating a surface rendering for visual colocalization analysis.During the process we will review methods for manipulating multidimensional arrays, including resizing, reshaping and conditional selection.

Introduction to Matlab

Bioimage Analyst

Matlab is a commercial software for numerical computing and statistical analysis that can be used to process and analyze multidimensional images. Within this session you will get familiar with the Matlab environment and its programming language. Among the topics addressed: matrix manipulation and advanced indexing, access to files, data inspection and basic plotting. This session will provide you the foundations that you need for the following modules, where you will manipulate images, extract measurements, and statistically analyze and visualize results.