Re-occurs among tags (visualisation, rendering, viewer, classification, ...)


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.


napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. It’s built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (e.g. numpyscipy). It includes critical viewer features out-of-the-box, such as support for large multi-dimensional data, and layering and annotation. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools (e.g. scikit-imagescikit-learnTensorFlowPyTorch), enabling more user-friendly automated analysis.

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Wolfram Mathematica (usually termed Mathematica) is a modern technical computing system spanning most areas of technical computing — including neural networksmachine learningimage processinggeometrydata sciencevisualizations, and others. The system is used in many technical, scientific, engineering, mathematical, and computing fields.


Dragonfly is a software platform for the intuitive inspection of multi-scale multi-modality image data. Its user-friendly experience translates into powerful quantitative findings with high-impact visuals, driven by nuanced easy-to-learn controls.

For segmentation: It provides an engine fior machine Learning, Watershed and superpixel methods, support histological data .

It offers a 3D viewer, and python scripting capacities .

It is free for reserach use, but not for commercial usage.