A Densely Connected UNet3D Network Combined Attention Mechanism for Liver and Tumor Segmentation

Abstract

In recent years, liver cancer has become one of the five dangerous cancers due to the highest mortality ratios worldwide. Automatic tumor segmentation is a most important task to help radiologists and oncologists to analyze liver CT images. With the rapid development of Convolutional Neural Network (CNN), UNet2D have been widely applied in medical image segmentation. But 2D convolutions cannot extract more important spatial information, making it difficult for the network to learn powerful features between slices. In order to address the problems, we proposed a new densely connected UNet3D network combined attention mechanism (Att-DialResUNet3D) for liver and tumor segmentation. During coding and decoding stages, UNet3D used residual convolution with jagged structure blocks to decrease spatial hierarchical information loss. UNet3D applied an attention mechanism by using the long-range connections between the encoder and decoder to increase the ability of learning important information network. Dense connection decreases the gradient dissipation, and deep supervision can train the shallow layer more fully. We evaluated the proposed approach on the MICCAI 2017 liver tumor segmentation challenge (LiTS) dataset. Our approach preceded other research methods and has gained superior performance for liver tumor segmentation.