Dynamic adaptive residual network for liver CT image segmentation

Due to the gray values of liver and surrounding tissues and organs are resemblance in abdominal computed tomography(CT) images, it is difficult to accurately determine the boundary of liver images. To address the aforementioned issues, we propose a segmentation method based on dynamic adaptive residual network (DAR-net). Firstly, inspired by the U-Net network, a dynamic adaptive pooling strategy based on interpolation optimization is proposed to process all features in the pooling domain. Subsequently, the skip connections and a residual structure are introduced into the network to improve the generalization ability and convergence speed of DAR-net. Finally, removing irrelevant pixels and condition random fields are used to optimize the boundaries and textures of liver to avoid the inaccuracy of liver boundaries. Experiments on a standard 3DIRCADB dataset demonstrate that the DAR-net can obtain the average Dice score is 96.13%, which increases by 13.02% compared to the prediction result without any processing.

Automated summary

This study introduces a liver image segmentation method based on dynamic adaptive residual network, improving the accuracy and efficiency of liver image segmentation by optimizing the network structure and introducing methods such as conditional random fields.