# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 5 Discussion  
Given the time-consuming and imprecise nature of the traditional manual approach to measuring Cobb angle, there is a growing trend towards utilizing computers to automate the Cobb angle measurement process on X-rays images. The Cobb angle is calculated based on 12 thoracic vertebrae and 5 lumbar vertebrae. However, the human spine additionally comprises cervical vertebrae, the hip bone and the sacrum, the latter of which share many similar characteristics to the thoracic and lumbar vertebrae, thus posing a significant challenge to the computer's ability to detect vertebrae. Drawing on the traditional image processing method [6; 7; 8; 9], which necessitates the selection of parameters, an incorrect configuration can lead to erroneous outcomes. Furthermore, it is less effective for the processing of complex images, e.g., those containing noise or weak edges, and comparatively low treatment efficiency. The methods based on deep learning [10; 13; 14] have achieved certain results in vertebrae detection. However, the datasets used by these methods are all cropped X-ray images of the spine, or the user needs to box-select the spine to exclude the information on the X-ray that will interfere with the detection of the spine. In particular, the cervical vertebrae, which are very similar to the 17 vertebrae (12 thoracic vertebrae and 5 lumbar vertebrae) to be detected, will significantly impact the prediction results and cannot be automatically distinguished from thoracic vertebrae and lumbar vertebrae after \begin{table}
\begin{tabular}{c c c} \hline \hline  & **ICC (95\% CI)** & **MAD (\({}^{\circ}\))** \\ \hline Observer1 \& Auto & 0.897 & 3.13 \\ Observer2 \& Auto & 0.901 & 3.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Consistency analysis of Cobb Angle automatic measurement and manual method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Locating Points** & \
\begin{tabular}{c} **Modifier** \\ **Formula** \\ \end{tabular} & \(\mathbf{MAPE_{FT}}\) **(\%)** & \(\mathbf{MAPE_{MT}}\) **(\%)** & \(\mathbf{MAPE_{TI}}\) **(\%)** &
\begin{tabular}{c} **Accuracy of End** \\ **Vertebrae** \\ **Selection (\%)** \\ \end{tabular} \\ \hline
1 & & & 42.96 & 35.64 & 38.03 & 20.6 \\
2 & & ✓ & 26.73 & 18.58 & 21.06 & 34.4 \\
3 & ✓ & & 25.45 & 33.19 & 28.27 & 78.1 \\
4 & ✓ & ✓ & 15.32 & 10.87 & 11.36 & 83.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of positioning points and correction formula on Detection Results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **ICC (95\% CI)** & **MAD (\({}^{\circ}\))** \\ \hline Intra-observer & Observer1 & 0.951 & 2.24 \\ Observer2 & 0.958 & 2.16 \\ Inter-observer & 1 st & 0.940 & 2.63 \\
2 nd & 0.942 & 2.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of detection results of spinal key points. prediction. Therefore, these methods cannot accurately and automatically detect scoliosis without cropping or manually frame-selecting the X-rays of the input network. This paper presents a method that utilizes anteroposterior X-ray images of the entire spine without cropping. In order to resolve the problems mentioned, in addition to predicting the center point of each vertebra, the network also predicts four positioning points, thereby allowing for the filtering of points that are predicted to be above the first thoracic vertebra and below the last lumbar vertebra on the full spine X-ray images and thus realize the automatic detection of the Cobb angle. The consistency between observers and within observers was analyzed using the Intraclass Correlation Coefficient (ICC) with 95% confidence interval (CI). It was concluded that the automated measurement method was reliable. This study has several limitations. First, there is limited research on the selection of the end vertebrae, which may be biased for spines with slight scoliosis due to the close slope between adjacent vertebrae.