# Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines
## 3 Results and Discussions
### Segmentation Results  
Segmentation experiments were performed on 19 MR spine images including 85 vertebral bodies. All user ROIs were manually specified vertebral body regions. The DSC was used to evaluate the  
Figure 5: Results of the region of interest (ROI) detection. Green boxes are the results of using only vertebral body ROI, and brown boxes are the results of using both vertebral body ROI and intervertebral disc ROI. accuracy of segmentation, and the references were manually obtained by an expert [34]. DSC is defined as  
\[\text{DSC}=\ \frac{2|\mathbf{F}_{\text{manual}}\cap\mathbf{F}_{\text{result}}|}{| \mathbf{F}_{\text{manual}}|+\ |\mathbf{F}_{\text{result}}|} \tag{9}\]  
where \(\mathbf{F}_{\text{manual}}\) represents the manual references and \(\mathbf{F}_{\text{result}}\) represents the segmentation results of the proposed method. Table 1 shows the DSC results for 19 MR spine images. The average DSC was 89.91% (Table 1). This is a valuable result as it is comparable to the 90.97% achieved by [PERSON] et al. who used the square-cut algorithm that requires more user inputs than the proposed method [27]. Further, it is higher than the 81.33% reported by [PERSON] et al. who used the cube-cut algorithm [3]. The standard deviation score 2.77% is similar to that reported by [PERSON] et al. (2.2%) [27] and much smaller than that reported by [PERSON] et al. (5.07%) [3]. For the number of voxels and the vertebral body volume, the square-cut algorithm showed a difference of 3.83% on both, compared with manual segmentation results [27], and the cube-cut algorithm showed differences of 15.95% and 5.97%, respectively, compared with manual segmentation results [3]. Our method, on the other hand, showed differences of 3.05% and 2.78% respectively, compared with manual segmentation results. Our results are lower than the cube-cut algorithm and square-cut algorithm. While a direct comparison of the results obtained with different algorithms is difficult, owing to differences with respect to image contrast type, resolution, image quality, and the number of test datasets used in each study, it generally suggests a comparable performance range of our algorithm. The square-cut and cube-cut algorithms, which are based on the graph-cut method, have no ROI detection processes; therefore, these algorithms require the locations of all vertebral bodies. Furthermore, because the results depend on the cost of constructing the graph, these algorithms require users to adjust the parameters such as the number of nodes and edges according to the circumstances. On the other hand, the proposed method requires the user to draw just one vertebral body ROI. Table 2 shows the comparison of the user inputs between Square-cut and Cube-cut algorithms and our method. Square-cut and Cube-cut algorithms requires four parameters to adjust and seed points for all vertebral bodies, but our method only needs a single vertebral body ROI. This suggests that our method can maintain or improve the segmentation performance despite reduction in the number of user \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Volume of Vertebral Body (mm\({}^{3}\))**} & \multicolumn{2}{c}{**Number of Voxels**} & \multirow{2}{*}{**DSC (\%)**} \\ \cline{2-2} \cline{4-6}  & **The Proposed** & **Manual** & **The Proposed** & **Manual** & \\ \hline  
\(\mu\ \pm\ \sigma\) & 2255.23 \(\pm\) 429.94 & 2188.52 \(\pm\) 377.83 & 5798.44 \(\pm\) 1265.49 & 5641.56 \(\pm\) 1110.66 & 89.91 \(\pm\) 2.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dice similarity coefficient (DSC) results of the experiments. inputs, and the automated portion of the whole segmentation process increases, as compared with the existing methods. Figure 6 shows a representative segmentation result, wherein Figure 6b shows the manually segmented areas, and Figure 6c shows the results of the proposed method. Figure 6d shows the difference between the manual references and the result of the proposed method.