Orthanc aims at providing a simple, yet powerful standalone DICOM server. It is designed to improve the DICOM flows in hospitals and to support research about the automated analysis of medical images. Orthanc lets its users focus on the content of the DICOM files, hiding the complexity of the DICOM format and of the DICOM protocol.

Orthanc can turn any computer running Windows, Linux or OS X into a DICOM store (in other words, a mini-PACS system). Its architecture is lightweight and standalone, meaning that no complex database administration is required, nor the installation of third-party dependencies.

What makes Orthanc unique is the fact that it provides a RESTful API. Thanks to this major feature, it is possible to drive Orthanc from any computer language. The DICOM tags of the stored medical images can be downloaded in the JSON file format. Furthermore, standard PNG images can be generated on-the-fly from the DICOM instances by Orthanc.

Orthanc also features a plugin mechanism to add new modules that extends the core capabilities of its REST API. A Web viewer, a PostgreSQL database back-end, a MySQL database back-end, and a reference implementation of DICOMweb are currently freely available as plugins.


Voxelytic-Align is a commercial software provided as a service on cloud targetted to electron microscopy reconstruction (alignement, artifact correction...)


webKnossos is an open-source data sharing and annotation platform for tera-scale 2D and 3D image datasets.

The core features of webKnossos are:

  • fast 3D data streaming
  • share links to specific locations in the data
  • uniquely fast skeleton annotation (flight mode) and
  • efficient volume annotation
  • mesh rendering
  • collaboration and sharing tools

webKnossos facilitates image analysis workflows on multi-terabyte datasets, including visualization of raw and multi-modal microscopy data, distributed training data generation and proof-reading of automatic segmentation.

As a scientific resource, webknossos.org serves as a database for published image datasets including their annotations.





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 Deck.gl 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.

Web based application for deep learning cell classifier . Aim to replace cellprofiler analyst and advanced cell classifier . Under construction. 

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