## 1 Introduction
Limitations do nonetheless exist in conventional surgical navigation systems. Firstly, the conventional surgical navigation system only provides three medical image planes of the patient and a 3-D model, without detailed information. Unless very careful segmentations are performed, it does not provide the surgeon with accurate 3-D information about vital organs, such as optic nerves or ICAs, which can be damaged during surgery. Secondly, there is no form of appropriate warning system, designed to advise the surgeon when a surgical instrument approaches a patient's organs. Thirdly, conventional navigation systems take a long time to register the medical image, making it difficult to coordinate actions with the imagery in real time. The use of deep learning in computer-assisted surgery has evolved quicker than expected. Deep learning is good for solving long registration times--which has been one of the problems of conventional navigation systems. Deep learning algorithms have mostly been applied to image segmentation [20; 21; 22], as most deep learning investigations focus on imaging, with very few cases applied to real robots or other systems. [PERSON] et al. [23] applied deep learning for real time depth estimation from single monocular frames to the bronchoscopy field but its accuracy was limited to 1.5 mm. A navigation  
Figure 3: Endoscope blind regions. system with 1.5 mm accuracy would be suitable for bronchoscopy but not suitable for spinal surgery or TSA surgery that requires accuracy within 1 mm, while [PERSON] et al. [24] proposed tracked surgical instruments using concurrent segmentation and localization architecture. Their model overcomes the specular reflections and motion blur problem using heatmap regression. Thus, it is recognized as the state-of-the-art performance in Endovis challenge. However, they have a limitation in that deep learning was applied only to image segmentation field. [PERSON] et al. [25] performed pose estimation of the pedicle screw using two networks opaque-trained network and gradient trained network simultaneously. Nevertheless, their pose estimate error was within 1.92 \(\pm\) 0.55 mm. In this study, we firstly propose a U-Net-based navigation system, which provides a 3-D model of optical nerves and ICAs--which are not available in current commercial surgical navigation systems. One benefit of this proposed system is that it provides automatic segmentation of important organs, such as optical nerves and ICAs, quickly and also provides 3-D models and multi-planar imagery, giving surgeons a better understanding of individual patient anatomical structure. It also incorporates an alarm, which alerts the surgeon as the surgical tool approaches vital organs, allowing the operator to approach the surgical target cautiously. Finally, a phantom experiment was conducted to demonstrate the effectiveness of the U-Net-based surgical navigation system and the bendable endoscope and surgical tool. In addition, through the phantom experiment, the proposed U-Net based navigation system confirmed the accuracy of fiducial registration error (FRE) and target registration error (TRE) within 0.5 mm. This research was conducted with the approval of the Institutional Review Board of Yonsei University Gangnam Severance Hospital (3-2019-0343). Experiments conducted in this research can be seen in Supplementary Materials video.