仅用仿真数据训练，实现结肠镜下高精度实时几何估计
CoGE: Sim-to-Real Online Geometric Estimation for Monocular Colonoscopy
- 基于Retinex理论的光照感知监督，解决不同场景光照差异问题
- 通过小波分解提取结肠共性结构与局部特征，提升模型泛化能力
- 无需真实标注数据，在仿真与真实场景均达顶尖性能，适合临床部署
几何估计（包括深度估计和场景重建）是结肠镜手术中至关重要的技术，可为医生提供三维空间感知与导航支持。然而，由于结肠空间狭窄封闭，难以获取准确的几何真值；同时，仿真数据与真实数据之间存在显著特征差距，主要由伪影和光照差异导致。本文提出CoGE框架，实现结肠镜下的在线单目几何估计。首先，基于Retinex理论设计光照感知监督模块，以应对不同结肠镜场景中的光照多样性；其次，提出基于小波分解的结构感知模块，有效提取结肠的共性结构与局部特征。定量与定性实验表明，该模型仅在仿真数据上训练，即可在仿真与真实场景中均达到当前最优性能。
原文摘要 · Abstract (English)
Geometric estimation including depth estimation and scene reconstruction is a crucial technique for colonoscopy which can provide surgeons with 3D spatial perception and navigation. However, geometric ground truth in colonoscopy is difficult to obtain due to narrow and enclosed space of the colon, while there is a large feature gap between simulated data and realistic data caused by artifacts and illumination. In this paper, we present CoGE, a novel framework for online monocular geometric estimation during colonoscopy. Firstly, we propose an illumination-aware supervision module based on the Retinex theory to address illumination diversity in different colonoscopy scenes. Moreover, a structure-aware perception module is proposed based on wavelet decomposition to extract common structural and local features of the colon. Both quantitative and qualitative results demonstrate that the proposed model solely trained on simulated data achieves state-of-the-art performance in geometric estimation for both simulated and realistic scenes.
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