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This paper presents an end-to-end pixelwise fully automated segmentation ofthe head sectioned images of the Visible Korean Human (VKH) project based onDeep Convolutional Neural Networks (DCNNs). By converting classificationnetworks into Fully Convolutional Networks (FCNs), a coarse prediction map,with smaller size than the original input image, can be created forsegmentation purposes. To refine this map and to obtain a dense pixel-wiseoutput, standard FCNs use deconvolution layers to upsample the coarse map.However, upsampling based on deconvolution increases the number of networkparameters and causes loss of detail because of interpolation. On the otherhand, dilated convolution is a new technique introduced recently that attemptsto capture multi-scale contextual information without increasing the networkparameters while keeping the resolution of the prediction maps high. We usedboth a standard FCN and a dilated convolution based FCN for semanticsegmentation of the head sectioned images of the VKH dataset. Quantitativeresults showed approximately 20% improvement in the segmentation accuracy whenusing FCNs with dilated convolutions.

Comparison of the Deep-Learning-Based Automated Segmentation Methods for the Head Sectioned Images of the Virtual Korean Human Project