A multi-view graph neural network approach for magnetic resonance imaging-based diagnosis of knee injuries

Biyong Deng , Jiashan Pan , Xiaoyu Tang , Haitao Fu , Shushan Hu
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Abstract

The knee plays a pivotal role in the human anatomy, serving as a cornerstone for support, mobility, shock attenuation, and balance. Currently, magnetic resonance imaging (MRI) remains the preferred method for diagnosing knee injuries, including anterior cruciate ligament (ACL) tears and meniscal tears, due to its efficiency and accuracy in medical imaging. However, the interpretation and understanding of knee MRI images are time-consuming, laborious, require sufficient expertise, and are also prone to diagnostic errors. Thus, it is imperative to devise a computational method employing knee MRI for intelligent diagnosis of knee injuries, as this could expedite medical assessments by physicians, reduce costs, and substantially reduce the risk of misdiagnosis. Although several computational methods have been proposed to diagnose knee injuries, most rely heavily on local features in MRI images and exhibit low prediction accuracy. In this paper, we proposed a novel multi-view graph neural network, abbreviated as MVGNN, to identify knee injuries (specifically ACL tears and meniscal tears) by leveraging graph representations derived from multiple MRI views. Comprehensive experiments demonstrate that MVGNN achieves state-of-the-art results for diagnosing knee injuries, with a 5.9% improvement in accuracy on ACL data and a 6.5% improvement on Men data, compared to the second-best method, MVCNN.
基于磁共振成像的膝关节损伤诊断的多视图神经网络方法
膝关节在人体解剖学中扮演着关键的角色,是支撑、活动、减震和平衡的基石。目前,磁共振成像(MRI)仍然是诊断膝关节损伤的首选方法,包括前交叉韧带(ACL)撕裂和半月板撕裂,由于其在医学成像中的效率和准确性。然而,膝关节MRI图像的解释和理解是费时费力的,需要足够的专业知识,也容易出现诊断错误。因此,设计一种利用膝关节MRI对膝关节损伤进行智能诊断的计算方法势在必行,因为这可以加快医生的医疗评估,降低成本,并大大降低误诊的风险。虽然已经提出了几种计算方法来诊断膝关节损伤,但大多数方法严重依赖于MRI图像中的局部特征,预测精度较低。在本文中,我们提出了一种新的多视图图神经网络,简称为MVGNN,通过利用来自多个MRI视图的图表示来识别膝关节损伤(特别是ACL撕裂和半月板撕裂)。综合实验表明,与第二好的方法MVCNN相比,MVGNN在诊断膝关节损伤方面取得了最先进的结果,ACL数据的准确率提高了5.9%,Men数据的准确率提高了6.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
8.40
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