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3D reconstruction of cystoscopy holds substantial value in the observation and guided treatment of urological conditions. 3D models of the bladder, obtained through the reconstruction, can aid physicians in performing a rapid and comprehensive assessment of various conditions, such as detection of bladder cancer and follow-up surveillance throughout the patients' life. In recent years, significant advancements have been made in 3D reconstruction based on cystoscopy. However, the broader application of these advancements has been hampered by issues such as texture loss, slow computational speed, and susceptibility to interference. In this study, we have achieved, for the first time, a reconstruction of a dynamic cystoscopy scene using Neural Radiance Fields (NeRF). Unlike mesh-based 3D reconstruction, NeRF can restore scenes under conditions where the number of views and features are limited, thereby addressing the problem of texture loss that might arise from traditional 3D reconstruction algorithms. Additionally, to expedite the computation speed of NeRF, we employed Instant-NGP, an open-source software that accelerates NeRF computations using hash encoding. This potentially reduces computation time by a hundredfold, making it several tens of times faster than SfM when camera pose values can be streamed into the NeRF reconstruction. By comparing NeRF and SfM methods, we found that NeRF exhibits stronger resistance to interference than SfM, underscoring the formidable potential of NeRF as an innovative method in the field of endoscopy. In the future of robotic-assisted flexible cystoscopy, NeRF has the potential to provide rapid, robust, and comprehensive recording for remote diagnosis of bladder abnormalities.
Chen et al. (Fri,) studied this question.