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Automatic extraction of liver and tumor from CT volumes is a challenging taskdue to their heterogeneous and diffusive shapes. Recently, 2D and 3D deepconvolutional neural networks have become popular in medical image segmentationtasks because of the utilization of large labeled datasets to learnhierarchical features. However, 3D networks have some drawbacks due to theirhigh cost on computational resources. In this paper, we propose a 3D hybridresidual attention-aware segmentation method, named RA-UNet, to preciselyextract the liver volume of interests (VOI) and segment tumors from the liverVOI. The proposed network has a basic architecture as a 3D U-Net which extractscontextual information combining low-level feature maps with high-level ones.Attention modules are stacked so that the attention-aware features changeadaptively as the network goes "very deep" and this is made possible byresidual learning. This is the first work that an attention residual mechanismis used to process medical volumetric images. We evaluated our framework on thepublic MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset.The results show that our architecture outperforms other state-of-the-artmethods. We also extend our RA-UNet to brain tumor segmentation on theBraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNetachieves good performance on a brain tumor segmentation task as well.

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans