## 4 Conclusions  
A novel channel R-CNN network for thoracoscopic surgery navigation to assist surgeons in cutting manipulation is proposed. The excised contours of target regions are tracked through semantic segmentation by a Detection Network Head (DNH) in channel 1#. Then, the cutting curve indication is conducted using the Thorax Network Head (TNH) in channel 2#, which involves rough and refined cutting operations. Results from the indicators exhibit an _mIOU_ of 79.4% and 70.5% and _OPA_ of 83.2% and 78.1% for the target contour and cutting curve segmentation, respectively. The contribution of the framework modules to the final results is verified by an ablation study. The comparative experimental results illustrate that our framework could achieve accurate and robust navigation under complex surgery situations in a clinical environment. It is feasible to implement semantic segmentation and navigation for thoracoscopic surgery using the RNN network. Our proposed technique, while promising, has room for enhancement, particularly in clinical applications such as segmentation accuracy and real-time responsiveness. In future studies, we aim to optimize the system's efficiency by reducing network configuration parameters and implementing an inference acceleration refiner. This will be crucial for meeting the stringent demands of real-time surgical navigation. To further improve segmentation accuracy, we plan to refine the loss function to minimize surgical risks, especially in critical tissue areas. This enhancement is vital for ensuring patient safety and surgical precision. Considering the framework structure, we will explore the application of more natural scenes to adapt the real-time network, maintaining accuracy while significantly boosting efficiency. This adaptation is essential for the model's practical deployment in dynamic surgical environments. Additionally, to meet real-time surgical navigation requirements, this future study will further optimize the model architecture to improve real-time performance and response speed; explore the use of specialized hardware (such as GPU, TPU, etc.) for acceleration to improve the computational efficiency; introduce online learning mechanisms to enable models that learn and adjust in real-time from new data to adapt to constantly changing surgical environments; and design end-to-end systems. On the other hand, the proposed model is also subjected to potential limitations: under real-time requirements, the model may require more computing resources and storage space, which may limit its deployment in resource-constrained environments; real time surgical navigation involves sensitive medical data; therefore, strict data privacy protection and security measures are required; the model needs to have good robustness in real-time surgical environments and be able to handle data noise, uncertainty, and unexpected situations, which may be challenging and a key direction for our future study. By expanding the application of the proposed model and addressing current limitations, the channel R-CNN could become an invaluable tool in enhancing surgical outcomes and training interns. Conceptualization, C.Z. and D.J. ; Data curation, Y.C. and B.Z. ; Methodology, Y.C. ; Resources, D.J. ; Software, C.Z. ; Supervision, D.J. ; Validation, B.Z. ; Writing--original draft, C.Z. ; Writing--review and editing, C.Z. and Y.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding; the APC was funded by China Nuclear Power Engineering Co., Ltd.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Research Center for Big Data and Intelligent Measurement and Control from Beijing Jiaotong University (protocol code EA2024090101 and date of approval 2024.9.1). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.