Abstract
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.
Abstract (translated)
深度学习有可能通过启用结肠的三维重建来改进结肠镜检查，提供粘膜表面和病变的整体视图，并帮助识别未探索的区域。然而，由于缺乏大规模的真实数据集，开发稳健方法的努力受到了限制。我们提出了RealSynCol，这是一个高度逼真的合成数据集，旨在复制内窥镜环境。从10次CT扫描中提取的结肠几何形状被导入到一个虚拟环境中，该环境紧密模拟了术中的条件，并用逼真的血管纹理进行了渲染。生成的数据集中包含28,130帧图像，每张图像都配有一份地面实况深度图、光流数据、三维网格和相机轨迹。 为了评估现有的合成结肠数据集在深度估计和姿态估计任务上的性能，我们进行了一项基准研究。结果显示，RealSynCol的高逼真度和多样性显著提升了在临床图像上的一般化性能，证明它是一种开发支持内窥镜诊断的深度学习算法的强大工具。
URL
https://arxiv.org/abs/2602.08397
