U-Net Encoder Decoder Architecture for Cell Segmentation

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

External resources

• Link to image data source (ISBI Challenge)