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, our ColonNeRF outperforms existing methods on two benchmarks over four evaluation metrics. Notably, our LPIPS-ALEX scores exhibit a substantial increase of about 67%-85% on the SimCol-to-3D dataset. 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，精确的长序列结肠镜重建面临三大挑战：（1）结肠因蜿蜒曲折形态导致各段间存在显著差异；（2）简单与复杂折叠几何结构共存；（3）受限相机轨迹导致稀疏视角。为应对这些挑战，我们提出一种基于神经辐射场（NeRF）的新型重建框架ColonNeRF，利用神经渲染实现长序列结肠镜的新视角合成。具体而言，为分段重建整个结肠，ColonNeRF引入区域划分与整合模块，有效降低形状差异并确保每段几何一致性。为在统一框架中学习简单与复杂几何，ColonNeRF融入多级融合模块，从易到难渐进式建模结肠区域。此外，为克服稀疏视角带来的挑战，我们设计了DensiNet模块，在语义一致性引导下实现相机位姿稠密化。我们在合成与真实数据集上开展广泛实验评估ColonNeRF。定量结果表明，在四项评估指标下，ColonNeRF在两个基准测试中均优于现有方法。值得注意的是，在SimCol-to-3D数据集上，我们的LPIPS-ALEX得分提升了约67%-85%。定性可视化显示，我们的重建结果呈现出更清晰的纹理和更精确的几何细节。这充分证明了我们相较于现有最优方法的优越性能。