Algorithm 1.

Epithelial segmentation

Preprocess

Generate (n,m) patches with stride s

Calculate the respective ground-truth probabilities

Train

Initialize weights and bias

For i=1: N_epochs, do

Forward pass, predict

L1 Loss:

Backpropagate,

Update weights with Adadelta optimizer:

End for

Save model and weights

Test

Load model and weights

Pad image:

Slice image to p,q subimages,

Generate (n,m) patches with stride 4

Predict the probability of each pixel

Combine the predictions to form a gradient mask

Upscale the mask by factor of 4

Post-process

Threshold the mask

Smooth the mask edges with quadratic Bezier curve,