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 )




Fiji plugin for detecting, tracking and quantifying filopodia

Tumor Blood Vessels: 3D Tubular Network Analysis

Bioimage Analyst

In this session, we will implement a simple ImageJ macro to segment and analyze the blood vessel network of a subcutaneous tumor. The analysis is fully performed in 3D, and possible strategies to extract statistics of the network geometry and interactively visualize the results are also discussed and implemented.

MSRC Registration Toolbox


This python toolbox performs registration between 2-D microscopy images from the same tissue section or serial sections in several ways to achieve imaging mass spectrometry (IMS) experimental goals.

This code supports the following works and enables others to perform the workflows outlined in the following works, please cite them if you use this toolbox:

  • Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy10.1021/acs.analchem.8b02884

  • Next Generation Histology-directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy10.1021/acs.analchem.8b02885

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Set of Tools for super resolution microscopy



Preprocessing step for high-density analysis methods in super resolution localisation microscopy: it aims at correcting artefacts due to these approaches with based on Haar Wavelet Kernel Analysis.



Image correction software for chromatic shifts in fluorescence microscopy




Daybook 2 is the analysis software linked to argoligth slides. It tests the performance of microscopes on various levels: illumination homogeneity, field distortion, lateral resolving power, stage drift, chromatic aberrations, intensity response... It works with various file formats but requires the use of an argolight test slide. 



Assess the performance of the lasers, the objective lenses and other key components required for optimum confocal operation.

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MIPs for PSFs


The macro generates orthogonal projections from bead images along the lateral and axial dimensions which are displayed using a customized look-up-table to color code intensities. A Gaussian curve is fit to the intensity profile of a fluorescent bead image and full-with-at-half-maximum (FWHM) values are extracted, and listed next to theoretical values for comparison.