卷积神经网络基于MRI在半月板损伤诊断中的研究进展
Research progress of convolutional neural networks based on MRI in the diagnosis of meniscus injury
袁典 1杜昱峥 1魏德健 1张俊忠 2曹慧1
作者信息
- 1. 山东中医药大学智能与信息工程学院,济南 250355
- 2. 山东中医药大学第一临床医学院,济南 250355
- 折叠
摘要
半月板在维持膝关节稳固性方面发挥关键作用,半月板损伤是运动医学领域中常见的损伤,是导致膝关节骨关节炎形成的主要常见原因.MRI具有较高的特异性和敏感性,可以检测半月板的形态结构和膝关节内部信号,是诊断半月板损伤最佳医学图像技术之一.卷积神经网络作为深度学习的经典神经网络,在医学图像辅助诊断领域具有优越的能力,利用卷积神经网络基于MRI图像辅助诊断半月板损伤的相关研究也相继提出.本文全面综述了卷积神经网络在半月板MRI图像分割、检测以及分类中的应用,可以帮助读者了解基于MRI的卷积神经网络在半月板损伤诊断方面的研究进展,以期为半月板损伤的早期诊断与个性化治疗提供新方向.
Abstract
The meniscus plays a crucial role in maintaining knee joint stability,and meniscus injury is a common injury in the field of sports medicine,which is the main cause of knee osteoarthritis.MRI has high specificity and sensitivity,which can detect the morphological structure of the meniscus and internal signals of the knee joint.It is one of the best medical imaging techniques for diagnosing meniscus injuries.Convolutional neural networks,as a classic neural network in deep learning,have superior capabilities in the field of medical image assisted diagnosis.Research on the use of convolutional neural networks for MRI image assisted diagnosis of meniscus injury has also been proposed.This article provides a comprehensive overview of the application of convolutional neural networks in meniscus MRI image segmentation,detection,and classification.It can help readers understand the research progress of MRI based convolutional neural networks in meniscus injury diagnosis,and provide new directions for early diagnosis and personalized treatment of meniscus injury.
关键词
半月板损伤/磁共振成像/卷积神经网络/深度学习/图像分割/图像分类Key words
meniscal injury/magnetic resonance imaging/convolutional neural networks/deep learning/image segmentation/image classification引用本文复制引用
基金项目
国家自然科学基金(82074579)
国家自然科学基金(82374620)
出版年
2024
国家科技期刊平台
NSTL
万方数据