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

## TL;DR

This study compares deep learning segmentation methods for head images in the VKH dataset, demonstrating that dilated convolution-based FCNs significantly improve accuracy over standard FCNs.

## Contribution

It introduces and evaluates the use of dilated convolution in FCNs for improved segmentation accuracy in head sectioned images.

## Key findings

- Dilated convolution FCNs outperform standard FCNs by approximately 20% in accuracy.
- The use of dilated convolutions captures multi-scale context without increasing parameters.
- Enhanced segmentation results demonstrate the effectiveness of dilated convolutions in medical image analysis.

## Abstract

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.