This workflow segments glands from H&E stained histopathological images
from the Gland Segmentation Challenge (GlaS2015) using deep learning (UNet).
UNet implementation largely inspired from PyTorch-UNet by Milesial.
This workflow processes images of cells with discernible nuclei and outputs a binary mask containing where nuclei are detected.
The macro will segment nuclei and separate clustered nuclei in a 3D image using a distance transform watershed. As a result an index-mask image is written for each input image.
Performs 3D Gaussian blurring.