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. 2021 May 21;9(5):e14755.
doi: 10.2196/14755.

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Qiong Pan #  1   2 , Kai Zhang #  3   4 , Lin He  3 , Zhou Dong  5 , Lei Zhang  3 , Xiaohang Wu  6 , Yi Wu  7 , Yanjun Gao  8
Affiliations

Automatically Diagnosing Disk Bulge and Disk Herniation With Lumbar Magnetic Resonance Images by Using Deep Convolutional Neural Networks: Method Development Study

Qiong Pan et al. JMIR Med Inform. .

Abstract

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.

Keywords: deep learning; disk bulge; disk herniation; image classification; object localization.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Examples of lumbar MR images. (A) A sagittal lumbar MR image in which 5 IVDs are labeled. (B) A sagittal lumbar MR image in which 6 vertebral bodies are enclosed in boxes. (C) An axial lumbar MR image of a normal disk. (D) An axial lumbar MR image of disk bulge. (E) An axial lumbar MR image of disk herniation. L: lumbar vertebra; MR: magnetic resonance; S: sacral.
Figure 2
Figure 2
Overall diagnosis system. This system consists of 4 steps. First, vertebral bodies (L1, L2, L3, L4, and L5) in sagittal lumbar magnetic resonance images were automatically located by using the faster R-CNN, and the middle point of each vertebral body was calculated. Second, the axial images were grouped into 5 categories. Each category corresponded to an intervertebral disk (ie, the L1-L2, L2-L3, L3-L4, L4-L5, and L5-S1 intervertebral disks). Third, the intervertebral disk regions of interest in each axial MR image were segmented using the faster R-CNN. Finally, in each category, the region-of-interest images were classified as images of normal disks, disk bulge, and disk herniation using ResNet101. L: lumbar vertebra; R-CNN: region-based convolutional neural network; S: sacral.
Figure 3
Figure 3
The intervertebral disks (from L1-L2 to L5-S1) in each axial image were located by calculating directed distances. The red dot shows the middle point of each vertebral body in a sagittal image. The blue line depicts the directed distance from the red dot to a specific axial image. L: lumbar vertebra; S: sacral.
Figure 4
Figure 4
Results of the 3-class classification (normal disk, disk bulge, and disk herniation). (A) The average accuracies of the classification system (calculated using ResNet101) for the following five categories: L1-L2, L2-L3, L3-L4, L4-L5 and L5-S1. The rows and columns of all heat maps represent ground truth labels and predicted labels, respectively. The x-axis shows the five intervertebral disks. (B) A heat map of the classification accuracies for category L1-L2. The color scale expresses the accuracy. (C) A heat map of the classification accuracies for category L2-L3. The color scale expresses the accuracy. (D) A heat map of the classification accuracies for category L3-L4. The color scale expresses the accuracy. (E) A heat map of the classification accuracies for category L4-L5. The color scale expresses the accuracy. (F) A heat map of the classification accuracies for category L5-S1. The color scale expresses the accuracy. L: lumbar vertebra; S: sacral.
Figure 5
Figure 5
Appearance and functions of the reports of the web-based automatic diagnostic system. (A) This is the page for uploading a folder. (B) Diagnostic results in tabular form. (C) The diagnostic report in the Unified format. LDP: lumbar disk protrusion.
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