Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO‐UNet

Abstract

With the social and economic development and the improvement of people's living standards, smart medical care is booming, and medical image processing is becoming more and more popular in research, of which brain tumor segmentation is an important branch of medical image processing. However, the manual segmentation method of brain tumors requires a lot of time and effort from the doctor and has a great impact on the treatment of patients. In order to solve this problem, we propose a DO‐UNet model for magnetic resonance imaging brain tumor image segmentation based on attention mechanism and multi‐scale feature fusion to realize fully automatic segmentation of brain tumors. Firstly, we replace the convolution blocks in the original U‐Net model with the residual modules to prevent the gradient disappearing. Secondly, the multi‐scale feature fusion is added to the skip connection of U‐Net to fuse the low‐level features and high‐level features more effectively. In addition, in the decoding stage, we add an attention mechanism to increase the weight of effective information and avoid information redundancy. Finally, we replace the traditional convolution in the model with DO‐Conv to speed up the network training and improve the segmentation accuracy. In order to evaluate the model, we used the BraTS2018, BraTS2019, and BraTS2020 datasets to train the improved model and validate it online, respectively. Experimental results show that the DO‐UNet model can effectively improve the accuracy of brain tumor segmentation and has good segmentation performance.

Application Simulation Research Based on Visual Image Capture Technology in Sports Injury Rehabilitation

“…ally, the output result of the -dimensional map is obtained, which is Grad-CAM map obtained from the input (11) The two-dimensional FeatureMap is passed to the ReLU activation function. The output is shown in Formula (12).…”

“…To verify the IS effect of IRS-Net network, it is compared with the U-Net Medical IS network and the CA-Network medical IS network in literature [11], and the DO-UNet medical IS network in literature [9,11,12]. The data set used in the test is the Muskuloskeletal Radiographs Abnormalities Database To verify the IS effect of IRS-Net network, it is compared with the U-Net Medical IS network and the CA-Network medical IS network in literature [11], and the DO-UNet medical IS network in literature [9,11,12]. The data set used in the test is the Muskuloskeletal Radiographs Abnormalities Database (MURA) database, which is a data set of musculoskeletal radiographs.…”

Construction of multi-scale feature fusion segmentation model of MRI knee images based on dual attention mechanism weighted aggregation

“…Han Lihong et al compared the application value of different algorithms of convolutional neural network in MRI image analysis of patients with severe stroke, and summarized the advantages of U-Net deep learning in MRI image segmentation [ 7 ]. Jianshe Shi et al proposed that the automatic segmentation of cardiac MRI based on multi-input fusion network could improve the training speed, thereby improving the efficiency of diagnosis [ 8 ]. Huang Tongyuan et al conducted a study on brain tumor segmentation by magnetic resonance imaging based on DO-UNet model.…”