Liver Computed Tomography Image Segmentation Algorithm by Improved U-Net Fused with Dilated Convolution
To improve the accuracy of liver CT image segmentation and address the issue of unbalanced edge correction,a liver CT image segmentation algorithm based on an improved U-Net and fused dilated convolutions is proposed.This algorithm aims to resolve the mentioned problems.It employs an enhanced global information module with an improved attention feature mechanism,decomposes traditional atrous convolution into one-dimensional convolution,and integrates residual connections to strengthen contextual information.The encoder is used to filter image information in the U-Net network,and the improved U-Net module is fused with the atrous convolution module to achieve image segmentation through a mixed pooling layer.Experimental results on the MSD liver dataset show that the proposed algorithm outperforms other models in preserving the accuracy of edge information in liver CT images,with a D coefficient of 93.98%and a Q coefficient of 96.74%.The average segmentation time is only 57 ms.
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