多架构深度学习模型在肩袖损伤MRI分析中的应用与对比研究

Application and Comparative Study of Multi-Architecture Deep Learning Models in the MRI Analysis of Rotator Cuff Tears

摘要: 肩袖损伤是肩关节疾病中常见的临床问题，其诊断和分类依赖于磁共振成像(MRI)的准确分析。然而，手动分析肩关节MRI图像耗时且主观性强，限制了诊断的效率和一致性。近年来，深度学习技术在医学图像处理领域展现出巨大潜力，本研究旨在探讨多架构深度学习模型在肩关节MRI图像选择和肩袖肌肉分割中的应用，以提高肩袖损伤诊断的准确性和效率。本研究验证了深度学习技术在肩关节MRI图像处理中的有效性，成功实现了肩关节MRI图像选择和肩袖肌肉自动分割任务。通过多任务学习设计和先进架构的应用，模型在分类和分割任务中均表现出色，具有高度的准确性和稳定性。此外，本研究进一步对比了不同前沿深度学习架构(如卷积神经网络、目标检测网络、实例分割及基于自注意力机制的Transformer模型)的优劣与适用场景。未来研究可以进一步优化模型的泛化能力，并结合模型可解释性以应对更多临床场景的需求。本研究为肩袖损伤的早期诊断和精准治疗提供了新的技术手段和理论支持。

Abstract: Rotator cuff injuries are a common clinical problem in shoulder diseases, and their diagnosis and classification rely on accurate analysis by magnetic resonance imaging (MRI). However, manual analysis of shoulder MRI images is time-consuming and subjective, limiting the efficiency and consistency of diagnosis. In recent years, deep learning technology has shown great potential in medical image processing, and this study aims to explore the application of multi-architecture deep learning models in shoulder MRI image selection and rotator cuff muscle segmentation to improve the accuracy and efficiency of rotator cuff injury diagnosis. This study verifies the effectiveness of deep learning technology in shoulder MRI image processing and successfully realizes the task of shoulder MRI image selection and automatic rotator cuff muscle segmentation. Through the application of multi-task learning design and advanced architecture, the model performs well in classification and segmentation tasks, with high accuracy and stability. Furthermore, this study compares the advantages, disadvantages, and applicable scenarios of different cutting-edge deep learning architectures (such as CNNs, object detection networks, instance segmentation, and self-attention-based Transformer models). Future research can further optimize the generalization ability of the model and combine model interpretability to meet the needs of more clinical scenarios. This study provides new technical means and theoretical support for the early diagnosis and precise treatment of rotator cuff injury.