  A fully convolutional neural network was used to measure the evolution of the
volume fraction of two different Kirkendall pores during the homogenization of
Ti coated Ni wires. Traditional methods like Otsus thresholding and the largest
connected component analysis were used to obtain the masks for training the
segmentation model. Once trained, the model was used to semantically segment
the two types of pores at different stages in their evolution. Masks of the
pores predicted by the network were then used to measure the volume fraction of
porosity at 0 mins, 240 mins, and 480 mins of homogenization. The model
predicted an increase in porosity for one type of pore and a decrease in
porosity for another type of pore due to pore sintering, and it achieved an F1
Score of 0.95.
