Colonoscopy reconstruction is pivotal for diagnosing colorectal cancer. However, accurate long-sequence colonoscopy reconstruction faces three major challenges: (1) dissimilarity among segments of the colon due to its meandering and convoluted shape; (2) co-existence of simple and intricately folded geometry structures; (3) sparse viewpoints due to constrained camera trajectories. To tackle these challenges, we introduce a new reconstruction framework based on neural radiance field (NeRF), named ColonNeRF, which leverages neural rendering for novel view synthesis of long-sequence colonoscopy. Specifically, to reconstruct the entire colon in a piecewise manner, our ColonNeRF introduces a region division and integration module, effectively reducing shape dissimilarity and ensuring geometric consistency in each segment. To learn both the simple and complex geometry in a unified framework, our ColonNeRF incorporates a multi-level fusion module that progressively models the colon regions from easy to hard. Additionally, to overcome the challenges from sparse views, we devise a DensiNet module for densifying camera poses under the guidance of semantic consistency. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our ColonNeRF. Quantitatively, ColonNeRF exhibits a 67%-85% increase in LPIPS-ALEX scores. Qualitatively, our reconstruction visualizations show much clearer textures and more accurate geometric details. These sufficiently demonstrate our superior performance over the state-of-the-art methods.

翻译：结肠镜重建对于诊断结直肠癌至关重要。然而，精准的长序列结肠镜重建面临三大挑战：（1）结肠因其蜿蜒曲折的形态导致各段结构差异显著；（2）简单结构与复杂折叠几何形态共存；（3）因相机轨迹受限导致视点稀疏。为解决这些问题，我们提出一种基于神经辐射场（NeRF）的新重建框架——ColonNeRF，利用神经渲染技术实现长序列结肠镜的新视角合成。具体而言，为分片段重建完整结肠，ColonNeRF引入了区域划分与整合模块，有效降低形状差异性并确保各段的几何一致性；为在统一框架中同时学习简单与复杂几何结构，ColonNeRF嵌入了多级融合模块，由易到难逐步建模结肠区域；此外，为克服稀疏视点的挑战，我们设计了DensiNet模块，在语义一致性引导下对相机姿态进行稠密化处理。我们在合成数据集与真实数据集上开展了广泛实验以评估ColonNeRF。定量分析显示，ColonNeRF的LPIPS-ALEX评分提升67%~85%；定性结果则表明，我们的重建可视化呈现更清晰的纹理与更精准的几何细节。这充分证明了本方法相较于现有最优技术的卓越性能。