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首页> 外文期刊>JMIR Medical Informatics >Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study
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Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

机译：通过使用深卷积神经网络自动诊断磁盘凸起和磁盘疝气：方法开发研究

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Background Disk herniation and disk bulge are two common disorders of lumbar intervertebral disks (IVDs) that often result in numbness, pain in the lower limbs, and lower back pain. Magnetic resonance (MR) imaging is one of the most efficient techniques for detecting lumbar diseases and is widely used for making clinical diagnoses at hospitals. However, there is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists. Objective The aim of this study was to present an automatic system for diagnosing disk bulge and herniation that saves time and can effectively and significantly reduce the workload of radiologists. Methods The diagnosis of lumbar vertebral disorders is highly dependent on medical images. Therefore, we chose the two most common diseases—disk bulge and herniation—as research subjects. This study is mainly about identifying the position of IVDs (lumbar vertebra [L] 1 to L2, L2-L3, L3-L4, L4-L5, and L5 to sacral vertebra [S] 1) by analyzing the geometrical relationship between sagittal and axial images and classifying axial lumbar disk MR images via deep convolutional neural networks. Results This system involved 4 steps. In the first step, it automatically located vertebral bodies (including the L1, L2, L3, L4, L5, and S1) in sagittal images by using the faster region-based convolutional neural network, and our fourfold cross-validation showed 100% accuracy. In the second step, it spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy. In the third step, the accuracy for automatically locating the intervertebral disk region of interest in axial MR images was 100%. In the fourth step, the 3-class classification (normal disk, disk bulge, and disk herniation) accuracies for the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 IVDs were 92.7%, 84.4%, 92.1%, 90.4%, and 84.2%, respectively. Conclusions The automatic diagnosis system was successfully built, and it could classify images of normal disks, disk bulge, and disk herniation. This system provided a web-based test for interpreting lumbar disk MR images that could significantly improve diagnostic efficiency and standardized diagnosis reports. This system can also be used to detect other lumbar abnormalities and cervical spondylosis.
机译：背景技术盘疝和磁盘凸起是腰椎间盘（IVDS）的两种常见疾病，通常导致下肢的麻木，疼痛和腰部疼痛。磁共振（MR）成像是检测腰椎疾病最有效的技术之一，并且广泛用于在医院进行临床诊断。然而，缺乏有效地解释大量MR图像以满足许多放射科医生的要求。目的是本研究的目的是介绍一种用于诊断磁盘凸起和突出的自动系统，可以节省时间，并可有效地减少放射科学家的工作量。方法腰椎椎骨障碍的诊断高度依赖于医学图像。因此，我们选择了两种最常见的疾病 - 磁盘凸起和突出的研究科目。本研究主要是关于鉴定IVDS（腰椎椎骨[L] 1至L2，L2-L3，L3-L4，L4-L5和L5至Sacral Vertebra的位置，通过分析矢状和矢状物之间的几何关系通过深卷积神经网络轴向图像和分类轴向腰盘MR图像。结果该系统涉及4步。在第一步中，它通过使用更快的基于区域的卷积神经网络在矢状图像中自动定位椎体（包括L1，L2，L3，L4，L5和S1），并且我们的四倍交叉验证显示了100％的精度。在第二步中，它自发地识别每个轴向腰部磁盘MR图像中的相应盘，具有100％的精度。在第三步中，在轴向MR图像中自动定位感兴趣的椎间盘区域的精度为100％。在第四步中，L1-L2，L2-L3，L3-L4，L4-L5和L5-S1 IVDS的3级分类（正常盘，磁盘凸起和磁盘静脉脉搏）精度为92.7％，84.4 ％，92.1％，90.4％和84.2％。结论成功构建了自动诊断系统，可以对普通磁盘，磁盘凸出和磁盘静脉进行分类。该系统提供了一种基于Web的测试，用于解释腰磁盘MR图像，可以显着提高诊断效率和标准化诊断报告。该系统还可用于检测其他腰椎异常和颈椎病。

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