基于DRA-UNet模型的超声图像分割方法OA北大核心CSTPCD

Aiming at the problems that the ordinary convolution operation cannot focus on the key areas,the encoder cannot effectively extract the global context information,and the simple skip connection cannot capture the salient features,so that it is easy to cause that the segmentation image resolution is reduced,the important details are lost,and the small object information cannot be accurately captured,a UNet(DRA-UNet)model based on the expansion rate attention mechanism was proposed,and an ultrasonic image segmentation method based on this model was developed.On the basis of the UNet model,the expansion rate attention gate and the multi-scale convolution(ConvMulti)module were introduced.The expansion rate attention gate module used dilated convolution to obtain a larger receptive field,and combined the local region pixels of the encoder's semantic position into the upsampling region to achieve more efficient skip connections.The ConvMulti module was used to obtain more detailed high-level feature information to make the encoder more powerful.Experimental results show that the proposed model can effectively suppress image noise,and greatly improve the expression ability of features with strong robustness.Compared with the six classical segmentation methods,the proposed method achieves 72.25%,83.89%and 97.47%under the intersection over union,F1-value and accuracy,respectively.