Abstract
BackgroundDisk 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.ObjectiveThe 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.MethodsThe 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.ResultsThis 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.ConclusionsThe 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.
Highlights
Magnetic resonance imaging (MRI) is a widely used technique for detecting lumbar disorders, and its advantages include high image quality and noninvasive and ionization-free radiation
It spontaneously identified the corresponding disk in each axial lumbar disk MR image with 100% accuracy
The leading question is as follows: how can radiologists interpret massive amounts of magnetic resonance (MR) images quickly and accurately for real-world applications? Motivated by machine learning– and deep learning–based clinical practice [3,4,5,6], we propose an automatic diagnosis system for diagnosing disk bulge and disk herniation with MR images via deep convolutional neural networks (CNNs), which can reduce radiologists’ workload and provide the consistency required to produce standardized diagnosis reports
Summary
Magnetic resonance imaging (MRI) is a widely used technique for detecting lumbar disorders, and its advantages include high image quality and noninvasive and ionization-free radiation. A study [9] on texture features that were obtained from IVD MR images used three different classifiers (ie, the back-propagation neural network, k-nearest neighbor, and support vector machine classifiers) to classify normal disks and IVDs and achieved a maximum accuracy of 83.33%. Many other methods have been proposed to automatically diagnose IVD diseases based on MR images [10,11,12,13] Most of these models are for sagittal MR images, and there are very few studies that have used axial lumbar MR images, which are even more important in real clinical scenarios to identify disk bulge and herniation [13]. 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. There is a lack of efficient tools for effectively interpreting massive amounts of MR images to meet the requirements of many radiologists
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