This workflow creates and trains a Unet for segmenting cell images. The trained network is used to predict the segmentation of unseen data. Data: The training data is a set of 30 sections from a serial section Transmission Electron Microscopy (ssTEM) data set of the Drosophila first instar larva ventral nerve cord (VNC). The microcube measures 2 x 2 x 1.5 microns approx., with a resolution of 4x4x50 nm/pixel. The corresponding binary labels are provided in an in-out fashion, i.e. white for the pixels of segmented objects and black for the rest of pixels (which correspond mostly to membranes). (Source: http://brainiac2.mit.edu/isbi_challenge/home) The required extensions: - KNIME Deep Learning - Keras Integration - KNIME Image Processing - Deep Learning Extension