A medical image segmentation algorithm based on spatial and channel attention mechanisms and data augmentation

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With the development and progress of medical imaging technology, the resolution of medical images has been increasing, and a variety of high-definition imaging modalities such as CT, PET-CT and MRI have emerged. U-Net network has the advantages of simple network topology and small training set data requirement; thus, the field of medical image segmentation uses it extensively. However, U-Net also has some problems, such as edge loss of segmentation results, long training time and single application scenario. For the medical image segmentation problem, this paper proposes a method that combines channel attention and spatial attention and uses an improved join strategy to join the network structure. To address the problem of insufficient data volume of medical images, this paper performs a data augmentation operation on the dataset with elastic deformation. In addition, we use a local-global training strategy to further improve the performance of training on medical images. When compared to the original U-Net, the Dice coefficient and IOU metrics are significantly better when utilizing the method suggested in this work. After extensive experiments, the strategy proposed in this study can achieve good outcomes when facing medical image segmentation problems and has great potential.