Digital histology

The ultimate goal of the NET framework is to make images of networks processable by computers. Therefore we want to have a pixel based image as input, as output we want a representation of the network visible in the image that retains as much information about the original network as possible. NET achives this by first segmenting the image and then vectorizing the network and then extracting information. The information we extract is

• First and foremost the graph of the network. We find the crossings (nodes) and connections between crossings (edges) and therefore extract information about the neighborhood relations, the topology of the network.
• We also extract the coordinates of all nodes which enables us to embed them into space. We therefore extract information about the geometry of the network.
• Last but not least we track the radii of the edges in the extraction process. Therefore every edge has a radius which can be identified with its conductivity.

In the following we will first provide detailed instructions on how to install NET on several platforms. Then we describe the functionality and options of each of the four scripts that make up the NET framework.

Kappa is a Fiji plugin for Curvature Analysis.

It allows a user to measure curvature in images in a convenient way. You can trace an initial shape with a B-Spline curve in just a few clicks and then fit that curve to image data with a minimization algorithm. It’s fast and robust.

QuantCenter is the framework for 3DHISTECH image analysis applications. with the goal of helping the pathologists to diagnose in an easier way. QuantCenter, is optimized for whole slide quantification. It has a linkable algorithm concept that tries to provide an easy-to-use and logical workflow. The user has different quantification modules that he or she could link one after other to fine-tune or to speed up the analysis.

QuPath is open source software for Quantitative Pathology. QuPath has been developed as a research tool at Queen's University Belfast.

This is the "prediction step" of the Pyxit segmentation model builder. It is a learnable segmentation algorithm based on ground-truth images and segmentation mask. It learns a multiple output pixel classification algorithm. It downloads from Cytomine-Core annotation images+alphamasks from project(s), build a segmentation (pixel classifier) model which is saved locally. Typical application: tumor detection in tissues in histology slides.