Improved Multistage Edge-Enhanced Medical Image Segmentation Network of U-Net
Medical image segmentation accuracy plays a key role in clinical diagnosis and treatment.However,because of the complexity of medical images and diversity of target regions,existing medical image segmentation methods are limited to incomplete edge region segmentation and insufficient use of image context feature information.An improved Multistage Edge-Enhanced(MEE)medical image segmentation network of the U-Net,known as MDU-Net model,is proposed to solve these problems.First,a MEE module is added to the encoder structure to extract double-layer low-stage feature information,and the rich edge information in the feature layer is obtained by expanding the convolution blocks at different expansion rates.Second,a Detailed Feature Association(DFA)module integrating the feature information of adjacent layers is embedded in the skip connection to obtain deep-stage and multiscale context feature information.Finally,the feature information extracted from the different modules is aggregated in the corresponding feature layer of the decoder structure,and the final segmentation result is obtained by an upsampling operation.The experimental results on two public datasets show that compared with other models,such as Transformers make strong encoders for medical image segmentation(TransUNet),the MDU-Net model can efficiently use the feature information of different feature layers in medical images and achieve an improved segmentation effect in the edge region.
medical image segmentationMultistage Edge-Enhanced(MEE)moduleattention modulemultiscale featuredeep learning
万方数据
维普
