Pan Qiong, Zhang Kai, He Lin, Dong Zhou, Zhang Lei, Wu Xiaohang, Wu Yi, Gao Yanjun
School of Telecommunications Engineering, Xidian University, Xi'an, China.
College of Science, Northwest A&F University, Yangling, China.
JMIR Med Inform. 2021 May 21;9(5):e14755. doi: 10.2196/14755.
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.
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.
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.
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.
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.
椎间盘突出和椎间盘膨出是腰椎间盘的两种常见疾病，常导致下肢麻木、疼痛和腰痛。磁共振成像（MR）是检测腰椎疾病最有效的技术之一，在医院广泛用于临床诊断。然而，缺乏有效的工具来有效解读大量的MR图像，以满足众多放射科医生的需求。
本研究的目的是提出一种用于诊断椎间盘膨出和突出的自动系统，该系统可节省时间，并能有效且显著减轻放射科医生的工作量。
腰椎疾病的诊断高度依赖医学图像。因此，我们选择了两种最常见的疾病——椎间盘膨出和突出——作为研究对象。本研究主要是通过分析矢状面和横断面图像之间的几何关系来确定腰椎间盘（腰1至腰2、腰2至腰3、腰3至腰4、腰4至腰5以及腰5至骶1）的位置，并通过深度卷积神经网络对腰椎间盘MR横断面图像进行分类。
该系统包括4个步骤。第一步，使用基于区域的快速卷积神经网络在矢状面图像中自动定位椎体（包括腰1、腰2、腰3、腰4、腰5和骶1），我们的四重交叉验证显示准确率为100%。第二步，它能自动在每张腰椎间盘MR横断面图像中识别出相应的椎间盘，准确率为100%。第三步，在横断面MR图像中自动定位椎间盘感兴趣区域的准确率为100%。第四步，腰1至腰2、腰2至腰3、腰3至腰4、腰4至腰5和腰5至骶1椎间盘的三类分类（正常椎间盘、椎间盘膨出和椎间盘突出）准确率分别为92.7%、84.4%、92.1%、90.4%和84.2%。
成功构建了自动诊断系统，它能够对正常椎间盘、椎间盘膨出和椎间盘突出的图像进行分类。该系统提供了一个基于网络的测试来解读腰椎间盘MR图像，可显著提高诊断效率并规范诊断报告。该系统还可用于检测其他腰椎异常和颈椎病。
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