## 3 Results and Discussions
### Robustness Analysis  
In order to evaluate the robustness of the proposed DNH module for thoracoscopic surgery region segmentation, we conducted comparison experiments using clinical surgery videos with a resolution of 1280 \(\times\) 720 at 24 fps. Surgical images under different complex backgrounds were input into the DNH module, and the region segmentation results of three test videos captured in one surgery are as shown in Figure 10, which effectively handles various states of surgical instrument occlusion. There rarely exists an abrupt direction change in the thoracoscopy angle during surgery; therefore, the segmentation of target regions is conducted based on the frames ordered in a small time period. Electrospurgery focus is indicated by the segmented mask, by which the chief surgeons obtain clearer cutting views. The proposed DNH can achieve robust segmentation of critical areas in clinical video sequences. As given in Figure 11, four main states with frequent occurrence including appliance occlusion (AO), tissue occlusion (TO), partial occlusion (PO), and complete visible (CV) are presented. It is concluded from Figure 11 that the proposed channel R-CNN effectively produces clear cutting curves in complex situations, which reduces the interferences from background pixels. To further analyze the cutting indication performance of the proposed method for thoracoscopic surgery, visual segmentation on extra real surgery videos from cooperative units is carried out, using images with a resolution of 1280 \(\times\) 720 at 24 fps from the cutting curve dataset. Table 4 illustrates the total number of frames and the percentage of frames with different states for each video sequence on cutting curve segmentation. The table outlines the channel R-CNN model's segmentation efficiency across eight video segments, showcasing the distinct percentages of events like AO, TO, PO, and CV. Variations in event \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(I_{1}\) & \(I_{2}\) & PSNR & mIOU & OPA & SMA & DC & Time \\ \hline DenseNet [57] & 2.1 & 0.2 & 35.62 & 61.2\% & 73.9\% & 72.7\% & 69.6\% & **20.4** \\ +TNH & 1.8 & 0.2 & 35.74 & 63.4\% & 76.3\% & 75.1\% & 73.7\% & 21.2 \\ +Leapfrog & **1.6** & **0.1** & **35.97** & **70.5\%** & **78.1\%** & **80.2\%** & **81.3\%** & 21.8 \\ Mask R-CNN [15] & 1.7 & **0.1** & 35.83 & 68.6\% & 77.5\% & 79.7\% & 80.2\% & 22.1 \\ U-Net [55] & 1.7 & 0.3 & 34.48 & 67.5\% & 77.4\% & 78.2\% & 80.5\% & 24.7 \\ \hline \end{tabular}
\end{table} 
Table 3: \(I_{1}\) and \(I_{2}\) error and commonly applied evaluation metrics for the cutting curve segmentation using thoracic histological images. Time represents the processing time spent on one frame; the unit is ms. Bold results refer to the best performance. Figure 10: Target region segmentation by the DNH in different clinical videos. (**a–c**) Three individual test videos from one surgery. distribution are evident, with average AO event percentages spanning from approximately 16.54% in v8 to 35.09% in v1. The TO event percentages fluctuate notably, ranging from 18.17% in v4 to 48.48% in v8. Meanwhile, PO event percentages average around 16.88%, with v3 exhibiting the highest at 19.49% and v8 the lowest at 9.27%. CV event percentages show a range from 5.47% in v2 to 32.13% in v4. The average number of frames across segments is 669, with v5 recording the highest at 1320 frames and v8 the lowest at 284 frames. The average processing for each frame remains stable at 41.6 ms. These quantitative metrics highlight the diverse performance characteristics of the model across different video segments, indicating the influence of event composition, frame count, and duration on segmentation efficiency, which also indirectly illustrates the stronger segmentation robustness of the proposed model. Table 5 shows the segmentation performance including the _mIOU_, _OPA_, _SMA_, _DC_, and time cost per frame (ms) for each video sequence to evaluate the efficiency and robustness of the proposed model. The average speed of our system is 23.6 fps in videos.