Research On Knee Cartilage Segmentation Method Based On MRI Image

The knee joint is one of the important joints that bear heavy pressure and motion,and is also a common site of sports injuries and diseases.Knee arthritis is more common in the cartilage area,so the study of knee cartilage has great practical significance.In medical imaging diagnosis,accurate segmentation of the knee joint can help doctors better observe and diagnose the lesion,and improve the diagnostic accuracy and treatment effect.At the same time,the segmentation of knee joint can also be applied to computer-aided surgery planning,disease monitoring and rehabilitation evaluation.However,in the clinical application neighborhood,traditional deep learning segmentation methods do not show enough accurate results,and there is a lack of open source data sets based on Chinese patients.Due to the large differences in physiological structure and pathological changes of different populations,the existing knee data sets at home and abroad may have problems such as data distribution deviation and model generalization performance degradation when applied to Chinese patients.Based on this,the research work of this paper is as follows:Firstly,an improved SwinUNet model is proposed to segment knee cartilage.Compared with other advanced models applied in this field,the accuracy of cartilage segmentation is significantly improved,and the improved model also has excellent computational performance when dealing with a large amount of data.Compared with the traditional UNet model,the improved model adopts the Swin Transformer encoder and decoder,uses the local and global attention mechanism to efficiently extract image features,and captures the local and global information in the process of gradually reducing the resolution,which significantly improves the accuracy of cartilage segmentation.Secondly,a knee MRI dataset containing 47 patients is obtained and labeled,and the MRI image bones and cartilage of 897 patients are annotated.The dataset is open sourced to improve the application effect of the knee image segmentation model in Chinese patients.Finally,in view of the low accuracy of cartilage segmentation of SwinUNet model,the residual network and SwinUNet were combined,and the bottleneck layer of SwinUNet was replaced with the fifth layer of ResNet34 network to improve the expression ability of image features and the acquisition of depth information,so as to improve the segmentation accuracy of cartilage region.