Contents

Software for computing single molecule localization accuracies and resolution measures

The FandPLimitTool is a GUI based software module that allows users to calculate the limits to the accuracy with which parameters can be estimated from single molecule imaging data. The software supports calculation of limits for the 2D/3D location estimation problem and the 2D/3D distance-estimation/resolution problem.

A user comes to the Facility: “I’ve got a set of 2 channels 3D images where objects are overlapping. I think the overlap might not be the same from object to object. I would like to quantify the physical overlap and get a map of quantifications”. Your mission: write the appropriate macro, knowing a user might always change her/his mind, and ask for more… Ready to take on the challenge ?

Holovibes is a free software dedicated to the calculation of holograms in real-time. Input interferogram data can be grabbed from a digital camera or loaded from files recorded beforehand. Massive amounts of data can be handled robustly at high throughput, saved to disk, and visualized in real-time without any risk of frame dropping thanks to the use of several configurable input and output memory buffers.

autoQC encapsulates a number of routines for performing microscope quality controls. From a few input images, it generates computer-friendly (i.e. CSV) data with numerical parameters for quality measures (resolution, field of view illumination, chromatic shift, stage reproducibility).

Basic image analysis for life scientists with a non-engineering background. The main goal is to teach how to address and solve scientific questions by state of the art image analysis strategies.

The goal of mamut2r is to imports data coming from .xml files generated with the Fiji MaMuT plugin for lineage and tracking of biological objects. {mamut2r} also allows to create lineage plots.

ImJoy is a plugin powered hybrid computing platform for deploying deep learning applications such as advanced image analysis tools.

ImJoy runs on mobile and desktop environment cross different operating systems, plugins can run in the browser, localhost, remote and cloud servers.

With ImJoy, delivering Deep Learning tools to the end users is simple and easy thanks to its flexible plugin system and sharable plugin URL. Developer can easily add rich and interactive web interfaces to existing Python code.

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.

The macro will segment nuclei and separate clustered nuclei in a 3D image using a distance transform watershed. As a result an index-mask image is written for each input image.

This suite provides plugins to enhance 3D capabilities of ImageJ.

Performs 3D Gaussian blurring.

Performs watershed algotirhm with ij-1.52i.jar. legacy:ij.plugin.filter.EDM("watershed").

The macro will segment nuclei and separate clustered nuclei using a binary watershed. As a result an index-mask image is written for each input image.

Multimodal image registration based on manual selection of matching pairs of landmarks. This image registration workflow is based
on MATLAB’s image processing toolbox using the identification of sites of clathrin-mediated endocytosis by correlative light electron microscopy (CLEM) as an example.

Tracking tools, such as TrackMate, produce tracks and their role stops there. However, tracks are just an intermediate data structure in the workflow. Their subsequent analysis produces the numbers upon which scientific conclusions are made. The track analysis is most often specific to the scientific question to be addressed, and therefore tracking tools remain generic and seldom include specialized analysis modules. Another toolset is required for track analysis; this workflow focuses on using MATLAB.

Five different samples of Drosophila melanogaster Kc167 cells were stained with Hoechst 33342, a DNA stain. The last sample (labeled nodsRNA) is of wild-type cells. Each of the other four samples (labeled 48, 340, Anillin, and mad2) has a different gene knocked down by RNAi.

The sample preparation is described in more detail by [Carpenter et al. (Genome Biology, 2006)(http://genomebiology.biomedcentral.com/articles/10.1186/gb-2006-7-10-r1…)