# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 4 Conclusions and Future Perspectives  
In this study, we mainly investigated the Lenke classification problem for scoliosis and proposed a novel automatic Lenke classification framework. First, we used deep networks such as Unet++ to segment the vertebrae in the spine X-ray images and employed an effective post-processing strategy to overcome the spots and adhesions caused by the segmentation errors. We then focused on the shape representation of the segmented spine, and a new shape descriptor was designed to describe the details of the spinal curvature. With shape feature extraction and matching, a new Lenke classification algorithm for scoliosis was constructed with a classifier. For comparison and application, we also built another alternative Lenke classification option based on the automatic measurement of the Cobb angle. Finally, multiple experiments were conducted on a public dataset, including evaluating the segmentation networks, evaluating the shape descriptors, evaluating the classification strategies, and performing ablation experiments. The experimental results indicated that the proposed method achieved the best Lenke classification performance for scoliosis among the compared methods. The ablation experiments demonstrated that the modules in our shape descriptor were organically connected and supported by each other. Figure 18: Computational complexity requirements vs. classification performance. (**a**) Accuracy versus param size; (**b**) precision versus param size; (**c**) recall versus param size; and (**d**) F1-score versus param size. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Feature Extraction** & **Training** & **Testing** \\ \hline Shape context & 882.226 s & / & 6.623 s \\ CBoW & 12.021 s & 98.53 s & 2.157 s \\ TAR & 52.175 s & / & 6.248 s \\ Fourier descriptor & 5.136 s & / & 4.278 s \\ Swin transformer & / & 447.10 s & 0.137 s \\ Vision transformer & / & 4979.01 s & 0.132 s \\ Resnet101 & / & 933.02 s & 0.130 s \\ OURS & 0.983 s & / & 0.889 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Time consumption with different classification methods. To further highlight the technical improvements and contribution compared with the state-of-the-art methods, three aspects should be noted. First, we improved the overall framework and viewed the Lenke classification of scoliosis from the perspective of its microscopic shape on the basis of segmentation network. Second, we improved the shape representation through three novel strategies: the adaptive similarity matching based on key segment calculation, the tilt angle description, and the symmetric matching. These strategies are carefully designed for the needs of the Lenke classification of scoliosis, which can be used to obtain more shape details. Finally, a general post-processing method for vertebrae segmentation is proposed, which improved the segmentation results for all compared deep networks. Although the proposed method achieved superior results in the Lenke classification of scoliosis, there is still much room for improvement in terms of the objective evaluation indicators. This is attributed to two reasons. First, different Lenke types have very similar shapes, which is very hard to distinguish with human judgment. The rules of the Lenke classification dictate that it requires a very high level of segmentation and fine-grained shape representation, which is a great challenge. In addition, for fairness of the comparison and reproducibility of the results, we evaluated our method on a publicly available dataset that only included coronal plane X-rays of scoliosis. In fact, sagittal plane X-rays are an important supplement for diagnosing of scoliosis. However, under the present case, our method still significantly improved the existing methods and yielded satisfactory results with smaller data requirements, faster computation, and full automation.