In surgical oncology, screening colonoscopy plays a pivotal role in providing
diagnostic assistance, such as biopsy, and facilitating surgical navigation,
particularly in polyp detection. Computer-assisted endoscopic surgery has
recently gained attention and amalgamated various 3D computer vision
techniques, including camera localization, depth estimation, surface
reconstruction, etc. Neural Radiance Fields (NeRFs) and Neural Implicit
Surfaces (NeuS) have emerged as promising methodologies for deriving accurate
3D surface models from sets of registered images, addressing the limitations of
existing colon reconstruction approaches stemming from constrained camera
movement.
However, the inadequate tissue texture representation and confused scale
problem in monocular colonoscopic image reconstruction still impede the
progress of the final rendering results. In this paper, we introduce a novel
method for colon section reconstruction by leveraging NeuS applied to
endoscopic images, supplemented by a single frame of depth map. Notably, we
pioneered the exploration of utilizing only one frame depth map in
photorealistic reconstruction and neural rendering applications while this
single depth map can be easily obtainable from other monocular depth estimation
networks with an object scale. Through rigorous experimentation and validation
on phantom imagery, our approach demonstrates exceptional accuracy in
completely rendering colon sections, even capturing unseen portions of the
surface. This breakthrough opens avenues for achieving stable and consistently
scaled reconstructions, promising enhanced quality in cancer screening
procedures and treatment interventions.

通过将 NeuS 应用于内窥镜图像并辅以单帧深度图，我们在结肠切片重建方面引入了一种新的方法，该方法解决了单目结肠镜图像重建中的组织纹理表示不足和尺度混淆问题，通过严格的实验证明了其在渲染结肠切片方面的异常精确性，为稳定和一致缩放重建打开了新的途径，为癌症筛查和治疗干预提供了更好的质量。