# Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
## 3 Experiments and Analysis
### Ablation Experiment for the Proposed Shape Descriptor  
To examine the effectiveness of each module of the proposed adaptive shape descriptor, we conducted a series of ablation experiments to evaluate each strategy's contribution. Specifically, we selected Unet++ as the segmentation network, and the segmentation results are described according to the proposed shape descriptor and further used for the Lenke classification (see Section 2.5.2). Four shape descriptors should be marked for ablation evaluation: (1) S1 descriptor, which only used outline representation and matching (see Section 2.4.1); (2) S2 descriptor, which employed adaptive similarity matching weight based on S1 (see Section 2.4.2); (3) S3 descriptor, which improved the shape representation by the upper and lower boundaries based on S2 (see Section 2.4.3); and (4) S4 descriptor, which combined S3 and symmetric matching, i.e., the finally proposed shape descriptor for the X-ray spinal images (see Section 2.4.4). The experimental results for the four shape descriptors incorporated into the Lenke classification framework of scoliosis are listed in Table 1.  
Figure 15: The visual comparison of some examples to illustrate the effects of post-processing. GT: ground truth; Predict: without post-processing; OURS: the post-processing was used. Based on a comprehensive analysis of the above results, the following conclusions can be drawn:  
First, regardless of whether post-processing is used, Table 4 shows a steady increase in all metrics, which indicates that our proposed shape modules have a positive effect on the shape representation and Lenke classification of scoliosis. Specifically, S2 improved S1 in accuracy (\(\otimes\)5.8% and \(\sqrt{6}\)%), precision (\(\otimes\)2.5% and \(\sqrt{2}\).4%), recall (\(\otimes\)1.8% and \(\sqrt{3}\).9%), and F1-score (\(\otimes\)7.3% and \(\sqrt{8}\).3%). This is mainly because the proposed adaptive weigh assignment strategy for shape matching in S2 uses the most curved segments as the key factors, which effectively reflects the most discriminative features of the Lenke shape. In addition, S3 led to an obvious increment on S2 in accuracy (\(\otimes\)7.9% and \(\sqrt{7}\).2%), precision (\(\otimes\)31.2% and \(\sqrt{3}\).7%), recall (\(\otimes\)8.4% and \(\sqrt{6}\).8%), and F1-score (\(\otimes\)10.9% and \(\sqrt{10}\).3%), which demonstrates that the proposed shape description of each vertebra's horizontal tilt angle is a great feature supplement for the Lenke classification of scoliosis. Finally, our S4 method achieved the best results and improved S3 in all metrics. This not only indicates that our method overcomes the errors caused by rotation, but also shows that the organic combination of these shape representation strategies can play a significant role in the Lenke classification. Second, from the results of Table 5, we notice that the proposed framework combining Unet++ and S4 achieved the best performance in the Lenke classification, irrespective of whether the post-processing operation was used. This is because Unet++ provided superior segmentation results in both the objective indicators and subjective effects, which cause the proposed shape descriptor to be more effective. However, a particular phenomenon should be explained, which is that Segmenter + S4 achieved an inferior classification performance even though Segmenter surpassed Unet++ in the quantitative evaluation of the segmentation. This is mainly because the actual segmentation effect of Segmenter has some non-negligible errors in the boundaries of the vertebrae, which greatly influences the