Pancreas Segmentation Model Based on Deformable Residual and Cascading Encoding
In order to solve the problems of large pancreatic shape and position change,noise interference,and some small tar-gets in pancreas segmentation by deep convolutional neural networks,a pancreatic segmentation model DC U-net combining de-formable shrinkage residual block(DSRB)and cascading encoding module(CEM)is proposed.The DSRB is designed by using two deformable convolutions,an attention mechanism,and a residual structure.This method solves the problem of large changes in pancreatic shape and position through deformable convolution,and uses soft thresholding to reduce noise interference.CEM is used to fuse features,and the coding features are multiplexed to reduce the feature differentiation in the encoding and decoding stage,and strengthen the learning of small target features.The experimental results on the NIH public dataset show that the pro-posed DC U-net model achieves an average Dice similarity coefficient(DSC)of 87.26%,the average section over union(IOU)of 77.98%,and the segmentation accuracy is better than that of the comparison model.
国家科技期刊平台
NSTL
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