A deep attention network via high-resolution representation for liver and liver tumor segmentation

The segmentation of liver and liver tumor is an essential step for computer-aided liver disease diagnosis, treatment and prognosis. Although deep convolutional neural networks have contributed to liver and tumor segmentation, their architectures can not maintain spatial details and long-range context information. Besides, the fixed receptive fields of these networks limit the segmentation performance of livers and tumors with variant sizes and shapes. To address above problems, we propose a deep attention neural network which contains high-resolution branch and multi-scale features aggregation for cascaded liver and tumor segmentation from CT images. To be specific, the high-resolution branch can maintain the resolution of the input image and thus preserves the spatial details. The multi-scale features exchange and fusion enable the receptive fields of the network to adapt to liver and tumor with variant shapes and sizes. The appended attention module evaluates the similarities between every two pixels to model the long-range dependence and context information so that the network can segment liver and tumor areas located in distant regions. Experimental results on the LiTS and the 3D-IRCADb datasets demonstrate that our method can generate satisfying performance.