## 3 Results and Discussions
### Evaluation of Time and Computation Complexity  
The hardware of the channel R-CNN is given in the section \"Implementation and Transfer learning\", and a comparison of the processing efficiency between the proposed system and the other similar methods is depicted in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Questions** & **D1** & **D2** & **D3** & **D4** & **D5** & **Mean** & **Standard Deviation** \\ \hline
1 & 8.5 & 9.8 & 9.4 & 9.9 & 8.4 & 9.2 & 0.64 \\
2 & 8.3 & 8.9 & 8.4 & 8.6 & 8.8 & 8.6 & 0.23 \\
3 & 8.1 & 7.7 & 7.5 & 7.9 & 7.3 & 7.7 & 0.29 \\
4 & 8.0 & 8.5 & 8.9 & 8.5 & 8.0 & 8.4 & 0.37 \\
5 & 8.5 & 7.5 & 8.5 & 7.9 & 8.6 & 8.2 & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Survey results by questionnaire. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **v1** & **v2** & **v3** & **v4** & **v5** & **v6** & **v7** & **v8** \\ \hline Time (ms) & CON & 27 & 26 & 24 & 24 & **23** & 26 & 24 & 24 \\  & CUR & 23 & 22 & 23 & **21** & 22 & 23 & 22 & 23 \\  & CON & 78.3\% & 82.5\% & 79.6\% & 76.1\% & 73.8\% & 80.7\% & **83.4\%** & 80.9\% \\ _mIOU_ & CUR & 71.8\% & 70.3\% & 68.6\% & 72.1\% & **73.4\%** & 72.2\% & 70.6\% & 69.2\% \\  & CON & 77.4\% & 83.7\% & **84.5\%** & 83.2\% & 81.1\% & 82.6\% & 83.8\% & 81.0\% \\ _OPA_ & CUR & 78.5\% & 77.0\% & 76.1\% & 78.4\% & **80.6\%** & 77.3\% & 77.5\% & 79.1\% \\  & CON & 90.1\% & 86.8\% & 85.7\% & **91.2\%** & 88.5\% & 86.2\% & 89.5\% & 88.7\% \\  & CUR & 78.2\% & 79.8\% & **80.7\%** & 78.4\% & 78.6\% & 79.0\% & 78.3\% & 80.6\% \\ _DC_ & CON & 86.9\% & 89.2\% & **90.9\%** & 84.6\% & 88.1\% & 89.3\% & 90.8\% & 88.8\% \\  & CUR & 80.4\% & 76.5\% & 80.8\% & 79.1\% & **81.6\%** & 80.7\% & 81.2\% & 80.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation performance and time costs of one-frame processing for video sequences (CON: thoracic tissue contour segmentation; CUR: cutting curve segmentation). Bold results refer to the best performance. As given in Table 7, the processing time for one image is listed. DeepLab [25] achieves the highest efficiency among all the models of 46 ms/image; atrous convolution and probabilistic graphical models contribute to the improvement. U-Net [55] reaches a similar running time as DeepLab of 48 ms/image, which is superior to other existing methods. Due to the proposed loss function and multi-channel network structure, the channel R-CNN achieves a lower running efficiency of 53 ms/image compared to U-Net, while outperforming the FCN-8s [54] and manual features [56] methods with an improvement of 1 ms and 9 ms, respectively. Considering the segmentation performance of target regions and cutting curves, the accuracy of the proposed system compensates for the response efficiency. However, in view of the real-time and safety requirements of clinical applications, frame rates above 30/s are common, and the efficiency of the proposed model needs to be further optimized. Figure 12 shows the computation complexity in bytes for FCN-8s, DeepLab, U-Net, manual features, and the proposed channel R-CNN. It is concluded that DeepLab and U-Net achieve relatively lower complexity when facing different scales of input data. The proposed system and FCN-8s gain similar space complexity, and the channel R-CNN runs faster than FCN-8s, whose segmentation performance is also higher. Although the efficiency and computation complexity of the proposed model is slightly worse than DeepLab and U-Net, its segmentation results are obviously better. The channel R-CNN has taken the segmentation accuracy and efficiency into account concurrently, which has the potential for clinical application in thoracoscopic surgery. To further evaluate the practical utility of the proposed system, we sought the expertise of chest specialists to provide insights on the processing time in the context of real-world applications. The feedback from these medical professionals offers a valuable perspective on the system's readiness for use in clinical settings.