Segmentation for Athlete's Ankle Injury Image Using Residual Double Attention U-Net Model

Abstract

The image of an athlete's ankle joint injury can help to check whether the athlete's ankle joint is damaged, and plays a very important role in clinical diagnosis. To address the problem of poor segmentation effect of traditional athletes' ankle injury image segmentation algorithm, an ankle injury image segmentation algorithm based on residual double attention U-Net model is proposed. First, the region of interest is extracted from the original ankle injury image. After translation, rotation and turnover, the image data is expanded. Second, the residual structure is used to adjust the gradient propagation and residual feedback of the segmentation framework, extract the attribute information in the region of interest, and combine the two to retain more image features. Finally, combined with the double attention module to improve the weight ratio of image features, the athlete ankle injury image segmentation is realized in the image segmentation framework based on residual double attention U-Net model. The results demonstrate that the maximum values of DSC, ASSD, PM, and CR for the proposed algorithm are 0.93, 0.1, 0.96, and 0.95, respectively, and the F1 score is 95.7%, indicating that the segmentation effect of this algorithm is closer to the theoretical segmentation effect, and higher precision in segmentation, and the segmented image has a high degree of similarity to the original image, resulting in excellent segmentation performance. HIGHLIGHTS• We propose a segmentation using the Residual Double Attention U-Net model.• Adjusting the gradient propagation of the segmentation framework using the residual structure.• Solved the problem of low Correspondence Ratio and F1 values in traditional algorithms.• Using multiple data sets to test the application effect of proposed algorithm.