Artificial intelligence



NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

  • Get started with established pre-trained networks using built-in tools;
  • Adapt existing networks to your imaging data;
  • Quickly build new solutions to your own image analysis problems.



Orbit Image Analysis is a free open source software with the focus to quantify big images like whole slide scans.

It can connect to image servers, e.g. Omero.
Analysis can be done on your local computer or via scaleout functionality in a distrubuted computing environment like a Spark cluster.

Sophisticated image analysis algorithms incl. tissue quantification using machine learning, object segmentation and classification are build in. In addition a versatile API allows you to enhance Orbit and to run your own scripts.




automated open-source image acquisition and on-the-fly analysis pipeline (initially developped for analysis of mitotic defects in fission yeast)

maars workflow from publication





"An open source machine learning framework for everyone "

TensorFlow™ is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains.

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Isotropic Super-Resolution for EM


Super-resolve anisotropic EM data along low-res axis with deep learning.


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SuRVoS: Super-Region Volume Segmentation workbench

A volume is first partitioned into Super-Regions (superpixels or supervoxels) and then interactively segmented by the user providing training annotations. SuRVoS can then learn from and extend the annotations to the whole volume.

User interface of SuRVoS showing example annotation on soft x-ray tomography data



WND-CHARM is a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provides classification accuracy comparable to state-of-the-art task-specific image classifiers. WND-CHARM can extract up to ~3,000 generic image descriptors (features) including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are derived from the raw image, transforms of the image, and compound transforms of the image (transforms of transforms). The features are filtered and weighted depending on their effectiveness in discriminating between a set of predefined image classes (the training set). These features are then used to classify test images based on their similarity to the training classes. This classifier was tested on a wide variety of imaging problems including biological and medical image classification using several imaging modalities, face recognition, and other pattern recognition tasks. WND-CHARM is an acronym that stands for "Weighted Neighbor Distance using Compound Hierarchy of Algorithms Representing Morphology."

Generated features



"we propose a novel automatic 3D neuron reconstruction algorithm, named Rivulet, which is based on the multi-stencils fast-marching and iterative back-tracking. The proposed Rivulet algorithm is capable of tracing discontinuous areas without being interrupted by densely distributed noises." 

This plugin can be used with default parameters or with user-defined parameters.

Example image obtained from Rivulet Wiki website (


SLDC (Segment Locate Dispatch Classify)


SLDC is an open-source Python workflow. SLDC stands for Segment Locate Dispatch Classify. This framework aims at facilitating the development of algorithms for detecting objects in multi-gigapixel images. Particularly, it provides algorithm developers with a structure to define problem-dependent components of their processing workflow (i.e. segmentation and classification) in a concise way. Every other concern such as parallelization and large image handling are encapsulated by the framework. It also features a powerful and customizable logging system and some components to apply several workflows one after another on a same image. SLDC can work on local images or interact with Cytomine

Example image:

Toy image data

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Leaf Segmentation using Trainable Weka Segmentation plugin


An example macro introduced in the documentation page of the ImageJ plugin Trainable Weka Segmentation (in Fiji, it's bundled). A segmentation protocol based on machine learning. Full macro is available in the "Download" Link. 

This plugin can be trained to learn from the user input and perform later the same task in unknown (test) data. Weka: it makes use of all the powerful tools and classifiers from the latest version of Weka. Segmentation: it provides a labeled result based on the training of a chosen classifier. Trainable Weka Segmentation Complete macro example is at the end of the page.

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