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Pyxit segmentation model prediction

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Author(s): 
Raphaël Marée, Gilles Louppe, Loic Rollus, Olivier Caubo, Benjamin Stévens
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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. It is based on "Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees" http://orbi.ulg.ac.be/handle/2268/12205 and was used in "A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning" http://orbi.ulg.ac.be/handle/2268/162084?locale=en