Bronchoscope navigation is used for assisting physicians during bronchoscopic examinations. Existing methods for depth acquisition in bronchus scene are mainly classified into two types: compute vision-based depth estimation which calculates depth information by using structure from motion (SfM) , which shows poor result 1) . Liu et al. used a dual convolutional neural network to estimate depth map from RB images by using depth information estimated from SfM 2) , however, the preparation of the

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... training data is time consuming. To overcome the shortcomings of the aforementioned methods, we propose a method to estimate depth information by using cycle generative adversarial network (CycleGAN) 3) . This network does not need preparation of paired training data. Previous literature shows good performance to estimate realistic colonoscopic images from virtual colonoscopic images. We use this method to estimate a depth image from real bronchoscopic (RB) images. We use CycleGAN to find an RB-to-depth translator to generate the depth images from the RB images. We evaluate the generated depth images by using manually created depth