Cell Segmentation

This workflow uses Tensorflow2 to create and train 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) In order to run this example, you need a local Python installation that includes TensorFlow 2. TensorFlow 2 must be selected to be used for the "DL Python" nodes on the "Python Deep Learning" preferences page. Please refer to https://docs.knime.com/latest/deep_learning_installation_guide/#dl_python_setup for installation recommendations and further information.

This is a companion discussion topic for the original entry at https://kni.me/w/9t-815kIWJ7G557O