深度学习在膝关节MRI图像分割中的研究进展OA
Research progress of knee joint MRI image segmentation based on deep learning
随着人口老龄化程度的加重,以膝关节骨性关节炎(Knee osteoarthritis,KOA)为代表的慢性关节病发病率逐年升高.自动分割膝关节MRI图像可以显著提高诊断和治疗效率,减轻医师负担.传统的MRI图像分割技术存在步骤繁琐、精度低、泛化能力差等问题.近年来,深度学习在医学图像分析领域展现了巨大的潜力和优势,在对膝关节MRI图像的分割中,骨骼分割的Dice相似系数(Dice similarity coefficient,DSC)达到了98%…查看全部>>
With an increase in ageing population,the incidence of chronic joint diseases such as knee osteoarthritis(KOA)has increased year by year.Automatic segmentation of knee joint MRI images can significantly enhance the efficiency of diagnosis and treatment,reducing the burden on physicians.Traditional MRI image segmentation techniques are challenged by cumbersome processes,low precision,and limited generalization capabilities.In recent years,deep learning has sh…查看全部>>
向煜航;孟滔;刘佳奇;张杏林
赣南医科大学医学信息工程学院,江西 赣州 341000江西一脉阳光集团股份有限公司,江西 南昌 330000上海影禾医脉智能科技有限公司,上海 200336上海影禾医脉智能科技有限公司,上海 200336||滑铁卢大学大数据研究实验室,滑铁卢 N2L3G1
临床医学
膝关节深度学习图像分割磁共振成像
Knee jointDeep learningImage segmentationMagnetic resonance imaging
《赣南医科大学学报》 2025 (1)
68-73,6
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