集成全尺度融合和循环注意力的医学图像分割网络

The encoder-decoder network in deep learning has excellent performance in image feature extraction and hierarchical feature fusion,and is often used in medical image segmentation.However,the current mainstream encoding and decoding network segmentation methods still face two problems:1)in encoding and decoding stages,image feature information mined by a single network may be insufficient;2)encoder-decoder networks using simple skip connections cannot fully exploit the contextual information of full-scale features.Therefore,aiming at the shortcomings of the existing methods,an encoder-decoder network integrating full-scale feature fusion and RNN with attention for medical image segmentation is proposed.At first,the convolutional multi-layer perceptron(MLP) module combined with MLP is introduced in U-Net encoder to further expand the feature receptive field of the encoder.Secondly,by the full-scale feature fusion module,the skip connection features of each scale are effectively fused with coarse-grained information and fine-grained information.This operation reduces the semantic difference between the skip-connection features of each scale and highlights the key feature information of the image.Finally,the decoder refines the image feature information level by level through the proposed recurrent attention decoding module(RADU) combining recurrent neural network(RNN) and attention mechanism,which strengthens feature extraction while avoiding information redundancy,and obtains the final segmentation results.The proposed method is compared with the mainstream algorithms on BrainWeb,MRbrainS,HVSMR and Choledoch datasets,the image segmentation precision is improved in pixel accuracy and dice similarity coefficient.Therefore,experimental results show that by introducing the full-scale feature fusion module and the proposed RADU,the proposed method can achieve excellent segmentation results in image segmentation applications and has good noise robustness and anti-interference ability.