Lightweight U2-Net semantic segmentation model for medical images with multiscale asymmetric convolution
In clinical practice,semantic segmentation of medical images plays a vital role in detecting diseases,allowing doctors to accurately determine the patients'conditions and make more targeted treatment plans.Based on the U2-Net network structure,we designed a semantic segmentation model for medical images with more efficient operation and more accurate segmentation.The number of model parameters was reduced by replacing the traditional attention mechanism with a multi-scale asymmetric convolution kernel as well as by reducing the number of layers of the original U2-Net network.By changing the connection method of the U2-Net network and using the hopping connection of the U-Net++network,the model was made to pass the feature information to maintain integrity,reduce information loss,and make the segmentation edges more accurate and continuous.Considering the imbalance of positive and negative samples and other difficulties,we designed the binary cross entropy loss function(BCE Loss)to avoid the dominance of a large number of simple negative samples in the training process,the dice loss function(Dice Loss)to excavate foreground regions,and the multiple loss function to favor the structural similarity of the two graphs.Structural similarity of the two graphs(MS-SSIM Loss),a combined loss function of the multilevel structural similarity loss function(MS-SSIM Loss),is employed to supervise network optimization.Our experimental results show our algorithm improves the F1 score by 2.6％and 1.4％over the existing state-of-the-art network model(SOTA)on the DRIVE and STARE datasets and improves the DSC metric by 2.6％on the ISIC-2018 dataset.Visualization of the segmentation results indicate the network fully extracts the sample information and improves the semantic segmentation effect in the case of smaller samples.
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