  This paper presents an end-to-end pixelwise fully automated segmentation of
the head sectioned images of the Visible Korean Human (VKH) project based on
Deep Convolutional Neural Networks (DCNNs). By converting classification
networks into Fully Convolutional Networks (FCNs), a coarse prediction map,
with smaller size than the original input image, can be created for
segmentation purposes. To refine this map and to obtain a dense pixel-wise
output, standard FCNs use deconvolution layers to upsample the coarse map.
However, upsampling based on deconvolution increases the number of network
parameters and causes loss of detail because of interpolation. On the other
hand, dilated convolution is a new technique introduced recently that attempts
to capture multi-scale contextual information without increasing the network
parameters while keeping the resolution of the prediction maps high. We used
both a standard FCN and a dilated convolution based FCN for semantic
segmentation of the head sectioned images of the VKH dataset. Quantitative
results showed approximately 20% improvement in the segmentation accuracy when
using FCNs with dilated convolutions.
