构建高保真结肠内镜合成数据集，助力3D重建算法训练
RealSynCol: a high-fidelity synthetic colon dataset for 3D reconstruction applications
- 基于10例CT扫描重建真实结肠结构，渲染逼真血管纹理
- 包含28,130帧图像及深度图、光流、3D网格等标注
- 显著提升模型在临床图像上的泛化能力，适合医学视觉研究
深度学习有望通过实现结肠的3D重建，提供黏膜表面和病灶的全景视图，并帮助识别未探查区域。然而，鲁棒方法的发展受限于大规模真实标注数据的缺乏。本文提出RealSynCol，一个高度逼真的合成结肠数据集，旨在模拟内镜环境。从10例CT扫描中提取的结肠几何结构被导入虚拟环境，以真实血管纹理进行渲染。该数据集包含28,130帧图像，配对提供真实深度图、光流、3D网格和相机轨迹。我们开展基准测试，评估现有合成结肠数据集在深度估计与位姿估计任务中的表现。结果表明，RealSynCol的高真实感与多样性显著提升了模型在临床图像上的泛化性能，证明其是开发支持内镜诊断的深度学习算法的强大工具。
原文摘要 · Abstract (English)
Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces and lesions, and facilitating the identification of unexplored areas. However, the development of robust methods is limited by the scarcity of large-scale ground truth data. We propose RealSynCol, a highly realistic synthetic dataset designed to replicate the endoscopic environment. Colon geometries extracted from 10 CT scans were imported into a virtual environment that closely mimics intraoperative conditions and rendered with realistic vascular textures. The resulting dataset comprises 28\,130 frames, paired with ground truth depth maps, optical flow, 3D meshes, and camera trajectories. A benchmark study was conducted to evaluate the available synthetic colon datasets for the tasks of depth and pose estimation. Results demonstrate that the high realism and variability of RealSynCol significantly enhance generalization performance on clinical images, proving it to be a powerful tool for developing deep learning algorithms to support endoscopic diagnosis.
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