## 1 Introduction
Image-guided surgery navigation techniques have been widely applied in various clinical scenes [36; 37; 38]. The main purpose of image-guided surgery (IGS) is to provide help to surgeons in order to perform safer and less invasive procedures while removing tissue tumors, resulting in surgeries that are conducted more efficiently and less riskily. As for thoracoscopic surgery, [PERSON] et al. [14] developed a thoracoscopic surgical navigation system with real-time augmented image guidance to assess the potential benefits for minimally invasive resection of chest tumors, which improved the accuracy of tumor localization. [PERSON] et al. [39] described the evolution of thoracoscopic spine surgery from basic endoscopic procedures using fluoroscopy and anatomical localization through developmental iterations. Moreover, a virtual reality simulation system [40; 41], three-dimensional navigation [42; 43], intraoperative fluorescence visualization [44], CT imaging [45], integrated models [46], and holographic laser projection [47] models have been deployed for thoracoscopic surgeries. However, no existingstudies focus on the cutting path planning process of chest surgery. To our best knowledge, the indication difficulties for the clinical operations caused by complex backgrounds and huge computation costs have not yet been addressed. The channel R-CNN contributes to this field by offering a comprehensive solution that combines instance segmentation and surgical navigation. Its dual-channel architecture not only segments surgical areas with high accuracy but also guides cutting paths, providing a level of functionality and integration that surpasses existing multi-task models. By leveraging the strengths of DenseNet and the innovative use of parallel channels, the channel R-CNN sets a new standard in both segmentation and navigation capabilities, making it a valuable tool in the realm of computer-aided surgery. Moreover, the proposed framework based on the channel R-CNN is an effective attempt at thoracoscopic surgical navigation, which has the advantages of high accuracy, light weight, and fast real-time response. Moreover, influences from occlusion regions by surgical instruments are also reduced, and the robustness of our system is strongly proved by experiments. A flowchart of the proposed framework is given in the next section. The contributions of our system are listed as follows:  
1. A real-time image-guided thoracoscopic surgery navigation system for surgeon operation with target region segmentation and cutting path indication is proposed. To our best knowledge, this is the first trial in the cutting operation indication of thoracoscopic surgery;
2. The channel R-CNN network is innovatively designed, the Detection Network Head (DNH) and Thorax Network Head (TNH) operate in parallel to process the surgery navigation task;
3. The improved leapfrog algorithm is applied to refine the cutting curve generation based on rough segmentation results, and the combination with the region detection results ensures the accuracy of the cutting curve and reduces the surgery risk. This paper is organized as follows. Section 2 presents the Materials and Methods. Results and Discussions are given in Section 3. Section 4 presents the Conclusions.