Viv is a JavaScript library providing utilities for rendering primary imaging data. Viv supports WebGL-based multi-channel rendering of both pyramidal and non-pyramidal images. The rendering components of Viv are provided as layers, facilitating image composition with existing layers and updating rendering properties within a reactive paradigm.

Rendering a pyramidal, multiplexed immunofluorescence OME-TIFF image of a human kidney using additive blending to render four image channels into a single RGB image in the client.

QuantiFish is a quantification program intended for measuring fluorescence in images of zebrafish, although use with images of other specimens is possible. This package is geared towards analysis of fluorescent infection models. The software is designed to automate processing of images of single fish, and outputs results as a .csv file. Alongside measures of total fluorescence above a threshold, this package also introduces several measures for dissemination and distribution of fluorescence throughout the specimen.

QuantiFish User Interface

Histology Topography Cytometry Analysis Toolbox (histoCAT) is a package to visualize and analyse multiplexed image cytometry data interactively. It can also export data in.fcs data for further analysis using  a specialized cytometry sofwtare such as Flowjo. 

It can be run as a compiled standalone or from matlab.


ND-SAFIR is a software for denoising n-dimentionnal images especially dedicated to microscopy image sequence analysis. It is able to deal with 2D, 3D, 2D+time, 3D+time images have one or more color channel. It is adapted to Gaussian and Poisson-Gaussian noise which are usually encountered in photonic imaging. Several papers describe the detail of the method used in ndsafir to recover noise free images (see references).

It is available either in Metamorph (commercial version), either as command line tool. Source are available on demand.

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Deep learning based image restoration methods have recently been made available to restore images from under-exposed imaging conditions, increase spatio-temporal resolution (CARE) or self-supervised image denoising (Noise2Void). These powerful methods outperform conventional state-of-the-art methods and leverage down-stream analyses significantly such as segmentation and quantification.

To bring these new tools to a broader platform in the image analysis community, we developed a simple Jupyter based graphical user interface for CARE and Noise2Void, which lowers the burden for non-programmers and biologists to access these powerful methods in their daily routine.  CARE-less supports temporal, multi-channel image and volumetric data and many file formats by using the bioformats library. The user is guided through the different computation steps via inline documentation. For standard use cases, the graphical user interface exposes the most relevant parameters such as patch size and number of training iterations, while expert users still have access to advanced parameters such as U-net depth and kernel sizes. In addition, CARE-less provides visual outputs for training convergence and restoration quality. Any project settings can be stored and reused from command line for processing on compute clusters. The generated output files preserve important meta-data such as pixel sizes, axial spacing and time intervals.

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