Automatic extraction of liver and tumor from CT volumes is a challenging task
due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep
convolutional neural networks have become popular in medical image segmentation
tasks because of the utilization of large labeled datasets to learn
hierarchical features. However, 3D networks have some drawbacks due to their
high cost on computational resources. In this paper, we propose a 3D hybrid
residual attention-aware segmentation method, named RA-UNet, to precisely
extract the liver volume of interests (VOI) and segment tumors from the liver
VOI. The proposed network has a basic architecture as a 3D U-Net which extracts
contextual information combining low-level feature maps with high-level ones.
Attention modules are stacked so that the attention-aware features change
adaptively as the network goes "very deep" and this is made possible by
residual learning. This is the first work that an attention residual mechanism
is used to process medical volumetric images. We evaluated our framework on the
public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset.
The results show that our architecture outperforms other state-of-the-art
methods. We also extend our RA-UNet to brain tumor segmentation on the
BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet
achieves good performance on a brain tumor segmentation task as well.

提出了一种名为 RA-UNet 的三维残差关注分割方法，可精确提取肝脏感兴趣体积并从该体积分割肿瘤。结果表明，该架构在肝肿瘤分割任务上胜过其他最先进的方法，并在脑肿瘤分割任务上取得良好的性能表现。