A Review of the Methods on Cobb Angle Measurements for Spinal Curvature
<p>Schematic of Cobb angle measurements: the Cobb angle is defined as the angle between the extension line of the upper end plate of the most inclined vertebral body in a curved segment and the extension line of the lower end plate of the most inclined vertebral body below.</p> "> Figure 2
<p>Schematic of octa segmentation method proposed by Chockalingam, N. et al. [<a href="#B42-sensors-22-03258" class="html-bibr">42</a>].</p> "> Figure 3
<p>The X-ray images processed by the GVF-snake model [<a href="#B48-sensors-22-03258" class="html-bibr">48</a>].</p> "> Figure 4
<p>The structure of the improved U-Net [<a href="#B55-sensors-22-03258" class="html-bibr">55</a>]: Compared with the original U-net, Wang Z. [<a href="#B58-sensors-22-03258" class="html-bibr">58</a>] has made two modifications. First, in order to reduce the amount of calculation, modify conv (3 × 3) to conv (1 × 1) + conv (3 × 3) + conv (1 × 1). In order to reduce the overall parameters, conv (3 × 3) is replaced with conv (1 × 1) when dealing with the same dimension and dimension reduction calculation.</p> "> Figure 5
<p>The framework of S<sup>2</sup>VR [<a href="#B64-sensors-22-03258" class="html-bibr">64</a>]: SVR is used to capture the angle and landmark output correlation structure matrix, improved to obtain S2VR. Sun, H. et al. [<a href="#B64-sensors-22-03258" class="html-bibr">64</a>] introduced manifold regularization and trained with kernel alignment method.</p> "> Figure 6
<p>The structure of Boostnet [<a href="#B65-sensors-22-03258" class="html-bibr">65</a>]. The Boostnet consists of three parts: a convolutional layer for feature extraction, a boostlayer for removing outliers, and a multi-output layer for relieving the pressure of small datasets by capturing inter-landmark dependencies.</p> "> Figure 7
<p>The structure of the MVC-net [<a href="#B67-sensors-22-03258" class="html-bibr">67</a>]. MVC-net consists of three parts: a convolutional layer for feature extraction (the convolutional layers are connected with X modules for feature joint learning); a spine landmark output layer and a Cobb angle output layer.</p> "> Figure 8
<p>The architecture of RSN-U-net [<a href="#B97-sensors-22-03258" class="html-bibr">97</a>]: Similar to U-net, RSN-U-net is also composed of an encoder and decoder. The encoder section consists of four repeated encoder stacks. Each encoder stack contains two conv (3 × 3) convolutional layers and one max-pooling (2). Each decoder stack contains two conv (3 × 3) and one Upsampling (2).</p> "> Figure 9
<p>Block diagram and the architectures of Faster-RCNN and Resnet [<a href="#B15-sensors-22-03258" class="html-bibr">15</a>]: Yang, J. et al. (<b>a</b>) The entire DLA workflow; (<b>b</b>) The architecture and workflow of Faster-RCNN; (<b>c</b>) The architecture of Resnet. [<a href="#B15-sensors-22-03258" class="html-bibr">15</a>]’s algorithm using convolutional layer to obtain a feature map of the back. The algorithm finds the ROI in the feature map, pools it, and classifies it with a classifier.</p> ">
Abstract
:1. Introduction
2. The Challenges of Cobb Angle Measurement
3. Typical Application Scenarios & Preprocessing Methods
3.1. PDE Filters Used for Feature Enhancement
3.2. Preprocessing Methods Used to Deal with Parallax
3.3. Preprocessing Methods Used for Metal Artifacts Reduction (MAR)
4. Evaluation Methods of Cobb Angle in 2D Images
4.1. Image Enhancement Method for 2D Image
4.1.1. Semi-Automatic Method
4.1.2. Full-Automatic Method
4.2. Machine Learning Methods for 2D Image
4.2.1. Segment the Vertebral Body to Measure the Cobb Angle
4.2.2. Locate the Landmark of the Spine to Measure the Cobb Angle
5. Evaluation Methods of Cobb Angle in 3D Images
5.1. Image Enhancement Method for 3D Image
5.1.1. Semi-Automatic Method
5.1.2. Full-Automatic Method
5.2. Machine Learning Method for 3D Image
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Measure | Definition |
---|---|
Technical Error of Measurement (TEM) | |
Standard Deviation (SD) | |
Average Reliability Coefficient (R) | |
Intra- or Inter-class Correlation Co-efficient (ICC) | |
Mean Absolute Difference (MAD) | |
Mean Square Error (MSE) |
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Jin, C.; Wang, S.; Yang, G.; Li, E.; Liang, Z. A Review of the Methods on Cobb Angle Measurements for Spinal Curvature. Sensors 2022, 22, 3258. https://doi.org/10.3390/s22093258
Jin C, Wang S, Yang G, Li E, Liang Z. A Review of the Methods on Cobb Angle Measurements for Spinal Curvature. Sensors. 2022; 22(9):3258. https://doi.org/10.3390/s22093258
Chicago/Turabian StyleJin, Chen, Shengru Wang, Guodong Yang, En Li, and Zize Liang. 2022. "A Review of the Methods on Cobb Angle Measurements for Spinal Curvature" Sensors 22, no. 9: 3258. https://doi.org/10.3390/s22093258