## 3 Experimental Results
### Parameter Settings
The performance of verteb segmentation is evaluated by specificity, sensitivity, precision, overall accuracy, F1 score, Matthews correlation coefficient (MCC) [54], DICE similarity index (DSI) [55], and Jaccard similarity index (JSI) [56] for quantitative assessment. We drew the confusion matrix to perform classic evaluation using four variables: true positive, which refers to the patches segmented correctly as vertebrae in the ground truth; false positive, which refers to the patches that are not classified as vertebrae but classified as vertebrae by method; true negative, which refers to the patches that are correctly classified as background; and false negative, which refers to the incorrectly classified patches as background. The proposed method achieves excellent results in classical measurements with an accuracy of 93.3%, a sensitivity of 91.1%, and a specificity of 93.4%. These results also confirm the improvement in the MCC results and indicate that our segmentation results show very satisfactory agreement with the ground truth (Table 2). DSI measures the spatial overlapping between two binary images, and its value ranges from zero (nonoverlap) and one (perfect overlapped). The DSI is calculated for the individual vertebra and then averaged for overall vertebrae. Each vertebra in the segmentation reference is compared with the labels with which it has the largest overlapped. All automatic detected vertebrae, including the misclassified ones, are considered for this evaluation. Our proposed method improves the segmentation results of DSI with 86.1% similarity to the ground truth and JSI of 84.9% score (Table 3). This research demonstrates that our proposed method can learn the complex segmentation task of vertebrae. Classification and segmentation errors are most often found in the first and last vertebrae, in which the model has less context availability. These vertebrae are often invisible, thereby making their segmentation difficult. DICE score is lower for the starting thoracic vertebrae because of ribs and intervertebral influence that are connected to the vertebrae. Some vertebrae that are not shown in the label annotations are still segmented by our method, thereby causing misclassification and low DICE score. These factors mislead segmentation. To show the generalization of our proposed method, we utilize the CSI 2014 dataset on the vertebra segmentation (Table 4). \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **DSI (\%)** \\ \hline [PERSON] et al. [15] & 81.0 \\ [PERSON] et al. [44] & 85.0 \\ [PERSON] et al. [57] & 83.0 \\ [PERSON] et al. [58] & 87.0 \\ [PERSON] et al. [41] & 84.0 \\ Our method & 86.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Our method compared with state-of-the-art methods on vertebrae segmentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Specificity** & **Sensitivity** & **Precision** & **Overall** & **F1 Score** & **Matthews Correlation** \\  & **(\%)** & **(\%)** & **(\%)** & **Accuracy (\%)** & **(\%)** & **Coefficient (MCC) (\%)** \\ \hline CT image 1 & 85.6 & 96.8 & 86.1 & 91.1 & 91.1 & 82.8 \\ CT image 2 & 93.6 & 97.5 & 93.0 & 95.4 & 95.2 & 91.0 \\ CT image 3 & 90.0 & 96.6 & 90.2 & 93.2 & 93.3 & 86.7 \\ Overall & 89.7 & 96.9 & 89.7 & 93.2 & 93.2 & 86.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation metrics by the confusion matrix. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **DICE Similarity Index (DSI)** & **Jaccard Similarity Index (JSI)** \\ \hline CT image 1 & 84.1 & 83.7 \\ CT image 2 & 85.6 & 84.2 \\ CT image 3 & 88.7 & 87.0 \\ Overall & 86.1 & 84.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Segmentation results. Compared with the findings of other related methods, our obtained results are satisfactory. For example, a mean DICE score of 85% is obtained in our preliminary work [44] for thoracic and lumbar vertebrae segmentation from CT images by using deep belief networks modeling but that has complex deep network architecture.