# HTN: Hybrid Transformer Network for Curvature of Cervical Spine Estimation
## 3 Experiments
### Comparison with State-of-the-Art Methods  
We present the results of the model trained on the public dataset in Table 3. Furthermore, we also compared our method with many other state-of-the-art methods, as shown in Table 4. The model can achieve a very small SMAPE of 8.29% for all angles. Specifically, the model gains 4.73%, 16.22%, and 18.51% for the PT, MT, and TL regions, respectively, in terms of SMAPE. The Pearson correlation coefficients (R) for the three corresponding parts are 0.9643 (_p_ < 0.001), 0.8944 (_p_ < 0.001), and 0.9301 (_p_ < 0.001). The paired t-test shows no statistically significant differences between the ground truth and the prediction of our method. The average detection error of landmarks is 44.87. For the AASCE MICCAI 2019 challenge dataset, existing methods only provide the overall performance of SMAPE and MSE. In addition to these performance metrics, we provided the data for the PT, MT, and TL regions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**E** & **SE** & **TM** & **IB \& FF** & **HL** & **SMAPE (\%)** & **MAD (SD)** & **Confidence** & **R (_p-Value_)** & **MSE** \\ \hline P & & & & 14.25 & 2.26\({}^{\circ}\) (1.90\({}^{\circ}\)) & 5.98\({}^{\circ}\) & 0.9501 (\(<\)10\({}^{-6}\)) & 5.97 \\ P & & & P & 12.58 & 2.14\({}^{\circ}\) (1.87\({}^{\circ}\)) & 5.81\({}^{\circ}\) & 0.9527 (\(<\)10\({}^{-6}\)) & 5.84 \\ P & & P & P & 12.37 & 2.11\({}^{\circ}\) (1.93\({}^{\circ}\)) & 5.90\({}^{\circ}\) & 0.9526 (\(<\)10\({}^{-6}\)) & 5.24 \\ P & & P & P & P & 11.06 & 1.79\({}^{\circ}\) (1.86\({}^{\circ}\)) & 5.45\({}^{\circ}\) & 0.9619 (\(<\)10\({}^{-6}\)) & 5.24 \\ P & P & P & P & P & 12.55 & 1.92\({}^{\circ}\) (2.57\({}^{\circ}\)) & 4.71\({}^{\circ}\) & 0.9611 (\(<\)10\({}^{-6}\)) & 5.46 \\ P & P & & & 13.45 & 2.08\({}^{\circ}\) (2.73\({}^{\circ}\)) & 5.72\({}^{\circ}\) & 0.9538 (\(<\)10\({}^{-6}\)) & 5.53 \\ P & P & P & P & P & 11.83 & 1.91\({}^{\circ}\) (2.50\({}^{\circ}\)) & 4.85\({}^{\circ}\) & 0.9638 (\(<\)10\({}^{-6}\)) & 5.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation studies of each component of our model. ED (encoder–decoder network); SE (SE attention); TM (transformer module); IB&FF (intermediate branch and feature fusion); HL (hybrid loss); SMAPE (symmetric mean absolute percentage error); MAD (mean absolute difference; SD (standard deviation); Confidence 95% (confidence interval of the difference of the statistic); R (Pearson correlation coefficient); MSE (mean square error). All ablation study experiments were conducted on the CS799 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **PT** & **MT** & **TL** \\ \hline SMAPE (\({}^{\circ}\)) & 4.73\(\%\) & 16.22\(\%\) & 18.51\(\%\) \\ MAD (\({}^{\circ}\)) & 3.19\({}^{\circ}\) & 3.81\({}^{\circ}\) & 3.91\({}^{\circ}\) \\ Confidence (\({}^{\circ}\)) & 8.77\({}^{\circ}\) & 11.95\({}^{\circ}\) & 11.02\({}^{\circ}\) \\ R (\(p\)-Value) & 0.9643 (\(<\)10\({}^{-6}\)) & 0.8944 (\(<\)10\({}^{-6}\)) & 0.9301 (\(<\)10\({}^{-6}\)) \\ Paired \(t\) test (p-Value) & 0.075 (\(<\)10\({}^{-6}\)) & 0.613 (\(<\)10\({}^{-6}\)) & 0.635 (\(<\)10\({}^{-6}\)) \\ MSE & & 44.87 (All landmarks) & \\ Overall SMAPE (\%) & & 8.29\(\%\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with state-of-the-art methods on the public AASCE MICCAI 2019 challenge dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SMAPE (\%)** & **MAD (SD)** & **Confidence** & **R (_p-Value_)** & **MSE** \\ \hline HTN1 & 11.06 & 1.79\({}^{\circ}\) (1.86\({}^{\circ}\)) & 5.45\({}^{\circ}\) & 0.9619 (\(<\)10\({}^{-6}\)) & 5.24 \\ HTN2 & 13.03 & 2.22\({}^{\circ}\) (1.87\({}^{\circ}\)) & 5.89\({}^{\circ}\) & 0.9498 (\(<\)10\({}^{-6}\)) & 5.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of two network structures. HTN1 contains four residual modules (ID: 1, 2, 3, 4), while HTN2 contains three residual modules (ID: 2, 3, 4).