ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy

TL;DR

ColonNeRF introduces a neural radiance field framework for high-fidelity, long-sequence colonoscopy reconstruction, addressing shape dissimilarity, complex geometries, and sparse viewpoints to improve diagnostic imaging.

Contribution

The paper presents a novel ColonNeRF framework that employs region division, multi-level fusion, and pose densification modules for improved colon reconstruction.

Findings

67%-85% LPIPS-ALEX score improvement

Clearer textures and more accurate geometry in reconstructions

Superior performance over state-of-the-art methods

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.