Multi-scale Attention Learning for Abdomen Multi-organ Image Segmentation
Image segmentation technology is an important branch in the field of medical image research,and this technology helps doctors diagnose and treat cancer.In order to further improve the accuracy of image segmentation,a multi-scale axial attention model MAU-Net(multi-scale axial attention U-Net)is proposed in this paper for organ segmentation.Firstly,the model uses a deep residual network to extract image features in the encoder stage to improve the model's generalization ability.Secondly,a pixel fusion module(PFM)is added to the decoder to enhance the ability to extract feature position information by re-encoding and linearly enhancing the feature information of the encoder.Finally,a multi-branch axial attention module(MAM)is added between the decoders to capture contextual information and enhance the ability to identify key feature information.Experimental results on multiple multi-organ image data sets such as Synapse,ACDC,and SegTHOR show that MAU-Net can achieve better results in both organ recognition and edge prediction.
image segmentationorgan segmentationattention mechanismthoracic and abdominal organsdeep learning
万方数据
维普
