# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 3 Experiments and Analysis
### Ablation Experiment for the Proposed Shape Descriptor
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Shape**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-score**} \\ \cline{2-9}  & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) \\ \hline S1 & 0.618 & 0.644 & 0.370 & 0.396 & 0.615 & 0.638 & 0.530 & 0.555 \\ S2 & 0.676 & 0.704 & 0.395 & 0.420 & 0.633 & 0.677 & 0.603 & 0.638 \\ S3 & 0.755 & 0.776 & 0.707 & 0.737 & 0.717 & 0.745 & 0.712 & 0.741 \\ S4 & 0.765 & **0.786** & 0.777 & **0.804** & 0.729 & **0.756** & 0.752 & **0.779** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lenke classification results for the compared methods based on segmentation by U-Net++. \(\otimes\): the post-processing was not used; \(\surd\): the post-processing was used; the bold indicates the best results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1-score**} \\ \cline{2-9}  & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) & \(\otimes\) & \(\surd\) \\ \hline FPN combine S4 & 0.698 & 0.716 & 0.633 & 0.771 & 0.615 & 0.616 & 0.624 & 0.670 \\ PSPNet combine S4 & 0.629 & 0.667 & 0.626 & 0.773 & 0.513 & 0.517 & 0.564 & 0.617 \\ MANet combine S4 & 0.663 & 0.686 & 0.609 & 0.765 & 0.523 & 0.534 & 0.563 & 0.598 \\ Unet combine S4 & 0.672 & 0.696 & 0.630 & 0.767 & 0.601 & 0.616 & 0.615 & 0.633 \\ Segmenter combine S4 & 0.705 & 0.725 & 0.630 & 0.770 & 0.603 & 0.630 & 0.616 & 0.667 \\ Unet++ combine S4 & 0.765 & **0.786** & 0.777 & **0.804** & 0.729 & **0.756** & 0.752 & **0.779** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Lenke classification results for the compared methods based on segmented results by different deep networks. \(\otimes\): the post-processing was not used; \(\surd\): the post-processing was used; the bold indicates the best results. Lenke classification of scoliosis. Additionally, for Unet++, by broadening the main structure of Unet, it can capture the features of different levels and obtain clearer boundaries for the vertebrae, thus providing a more reliable benchmark for shape description. Furthermore, the post-processing operation plays a greater role in Unet++ than Segmenter by viewing Table 3, which also contributes to improving the classification effect. Finally, by comparing the results in Table 4 on whether post-processing was used, the proposed post-processing strategy improves the classification performance under all evaluation indicators. For example, the post-processing operation gains accuracy improvement with 2.6%, 2.8%, 2.1%, and 2.1% for S1, S2, S3, and S4, respectively, and gains F1-score improvements with 2.5%, 3.5%, 2.9%, and 2.7% for those four methods. Indeed, the results in Table 4 support a similar conclusion, i.e., that a post-processing operation improves the Lenke classification performance for different segmentation networks combined with S4. These improvements are even more significant, such as the post-processing operation gaining precision improvement with 13.8%, 14.7%, 15.6%, 13.7%, 14.0%, and 2.7% for FPN, PSPNet, MANet, Unet, Segmenter, and Unet++ combined with S4, respectively. This suggests that the proposed post-processing algorithm can effectively overcome the segmentation error and help to improve the scoliosis classification performance.