ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy

• URL: http://arxiv.org/abs/2312.02015v2
• Date: Thu, 21 Mar 2024 15:32:35 GMT
• Title: ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy
• Authors: Yufei Shi, Beijia Lu, Jia-Wei Liu, Ming Li, Mike Zheng Shou,
• Abstract summary: ColonNeRF is a novel reconstruction framework based on neural rendering for novel view synthesis of long-sequence colonoscopy. ColonNeRF reduces shape dissimilarity and ensures geometric consistency in each segment. Our reconstruction visualizations show much clearer textures and more accurate geometric details.
• Score: 20.617076071039282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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