ColonNeRF：长序列结肠镜的高保真神经重建
ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy
AI总结：
ColonNeRF是一种基于NeRF的长序列结肠镜重建框架，通过区域划分与整合、多级融合和DensiNet模块解决形状差异、复杂几何和稀疏视角挑战，显著提升重建保真度。
摘要：
结肠镜重建对于诊断结直肠癌至关重要。然而，准确的长序列结肠镜重建面临三大主要挑战：（1）由于结肠蜿蜒曲折的形状导致各段之间存在差异；（2）简单和复杂折叠的几何结构共存；（3）受限于相机轨迹导致的稀疏视角。为应对这些挑战，我们引入了一种基于神经辐射场（NeRF）的新重建框架，命名为ColonNeRF，利用神经渲染进行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引入了区域划分与整合模块，有效降低了形状差异并确保每个分段的几何一致性。为在统一框架中学习简单与复杂的几何结构，ColonNeRF结合了多级融合模块，逐步从易到难建模结肠区域。此外，为克服稀疏视角的挑战，我们设计了DensiNet模块，在语义一致性的指导下稠密化相机位姿。我们在合成和真实数据集上进行了大量实验以评估ColonNeRF。定量上，ColonNeRF在LPIPS-ALEX分数上提升了67%-85%。定性上，重建可视化显示了更清晰的纹理和更准确的几何细节。这充分证明了我们优于现有最先进方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
