## 3 Results and Discussions  
### Evaluations on Thoracic Tissue Contour Segmentation  
This section discusses the quantitative results for thoracic tissue contour segmentation, which are shown in Table 2. Four methods are applied for performance comparison. FCN-8s achieves competitive results due to its ability to capture spatial information at multiple scales; DeepLab utilizes atrous convolution (also known as dilated convolution) to effectively capture object boundaries and fine details in images; U-Net is commonly used \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Prediction Category (\(P^{\prime}\))** & **Prediction Category (\(N\))** \\ \hline Actual category (\(P\)) & True positive (\(TP\)) & False negative (\(FN\)) \\ Actual category (\(N\)) & False positive (\(FP\)) & True negative (\(TN\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix for the evaluation of detection performance. for biomedical image segmentation tasks and is designed to efficiently capture both local and global features through a U-shaped architecture with skip connections; manual traits refers to a manual feature extraction approach where handcrafted features are used for segmentation, and it involves extracting and selecting features based on domain knowledge and manual analysis. The average performance (indicated by _mIOU_, _OPA_, _SMA_, and _DC_) of the proposed DNH model is compared with these four baseline approaches using our dataset. Table 2 (Row 5) shows the segmentation performance of our model, which achieves 79.4% _mIOU_, 83.2% _OPA_, and 88.4% _SMA_ among the proposed dataset. The proposed approach outperformed the other baseline models with slightly better results. In the subclasses, the channel R-CNN reaches relatively good performance in PM, RP, and NT classification. Meanwhile, it only achieves a value of 73.5% for blood vessels. This is due to the fickle appearance of vessels, which adds to the model detection performance. For the comparison of processing efficiency, the channel R-CNN achieves an efficiency of 23.3 ms on one frame, which outperforms the other models with the leading edge of 1.1 ms, 3.9 ms, 3.2 ms, and 5.8 ms. We credit the segmentation improvement to the following two differences between our model and the baseline models: First, a two-stage structure was applied in the left channel; the RPN module (1 st stage) transfers the focusing region information to the DNH module (2 st stage) using the \"attention\" mechanisms. Second, a larger neural network, DenseNet, was adopted for the segmentation avoiding failing into the overfitting dilemma with a mass of parameters. As for the efficiency advantage, the channel R-CNN outperforms others due to its ability to integrate region proposal networks (RPNs) and achieve instance segmentation within a single framework. This integration allows for shared computation between the two tasks, leading to improved speed and performance. Additionally, the use of features shared across tasks helps reduce redundant computations. Segmentation outcomes of the thoracic tissue contour and surgical instruments are as given in Figure 8. Clear and complete borders of different regions are produced by the DNH channel. As given in Figure 9, we compare the segmentation performance of FCN-8s, U-Net, and the channel R-CNN for specific thoracic tissue categories. Examining the results presented in Figure 8a-c, it is concluded that the proposed system outperforms the existing approaches with the increasing rates of metrics varying from 1% to 11%. Feature maps from the DenseNet provide more explicit semantic characteristics for segmentation and propose obvious flags for boundary division. All the models gain better results for the pectoralis major (PM) and burnt spots (BS); this is due to their unique texture features and richer datasets. As for the \"Blood vessel\" (BV) and \"Nervous tissue\" (NT), there still exist much room for improvement; smaller morphological areas and chromaticity similar