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.

has function

This macro toolset offers additional click tools for the rapid annotations of ROI in ImageJ/Fiji.

The ROI 1-click tools can be setup with a predefined shape, and custom actions to perform upon click (Add to ROI Manager, Run Measure, Go to next slice, run a macro command...)

To install in Fiji, just activate the ROI 1-click tools 


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.

need a thumbnail

The Morphonet Python API provide an easy interface to interact directly with the MorphoNet server. Very useful to upload, download your dataset and superimpose on it any quantitative and quantitative informations.


MorphoNet is a novel concept of web-based morphodynamic browser to visualise and interact with complex datasets, with applications in research and teaching. 

MorphoNet offers a comprehensive palette of interactions to explore the structure, dynamics and variability of biological shapes and its connection to genetic expressions. 

By handling a broad range of natural or simulated morphological data, it fills a gap which has until now limited the quantitative understanding of morphodynamics and its genetic underpinnings by contributing to the creation of ever-growing morphological atlases.