Segmentation of Liver Tumors Based on Bottleneck Residual Attention Mechanism U-net

The segmentation of liver tumors is difficult due to the large amount of noise contained in medical CT images and the uneven size, location and tissue density of liver tumors similar to adjacent organs. The existing traditional full-convolutional neural network (FCN) method can achieve liver tumor segmentation by assigning category labels to each pixel in the input CT image, but the problems of small target missing or fuzzy target boundary segmentation still occur in the segmentation accuracy. In order to solve these problems, we propose a bottleneck residual attention mechanism U-net (BRA U-net) segmentation method for liver tumors. By introducing bottleneck residual modules, we can greatly reduce the computational load in very deep networks while solving the gradient disappearance problem. In addition, stacked attention modules can increase the proportion of effective features. In this paper, the proposed framework was tested on the public MICCAI2017 liver tumor segmentation dataset, and the dice similarity coefficient value reached 0.788, higher than other comparative segmentation networks. The generality of the proposed method was verified by using the 3D-IRCADb dataset. The results show that the proposed method has good segmentation effect and can provide a reliable basis for clinical diagnosis.