Accurate 3D modeling of human organs plays a crucial role in building computational phantoms for virtual imaging trials. However, generating anatomically plausible reconstructions of organ surfaces from computed tomography scans remains challenging for many structures in the human body. This challenge is particularly evident when dealing with the large intestine. In this study, we leverage recent advancements in geometric deep learning and denoising diffusion probabilistic models to refine the segmentation results of the large intestine. We begin by representing the organ as point clouds sampled from the surface of the 3D segmentation mask. Subsequently, we employ a hierarchical variational autoencoder to obtain global and local latent representations of the organ's shape. We train two conditional denoising diffusion models in the hierarchical latent space to perform shape refinement. To further enhance our method, we incorporate a state-of-the-art surface reconstruction model, allowing us to generate smooth meshes from the obtained complete point clouds. Experimental results demonstrate the effectiveness of our approach in capturing both the global distribution of the organ's shape and its fine details. Our complete refinement pipeline demonstrates remarkable enhancements in surface representation compared to the initial segmentation, reducing the Chamfer distance by 70%, the Hausdorff distance by 32%, and the Earth Mover's distance by 6%. By combining geometric deep learning, denoising diffusion models, and advanced surface reconstruction techniques, our proposed method offers a promising solution for accurately modeling the large intestine's surface and can easily be extended to other anatomical structures.

翻译：精确的人体器官三维建模在构建虚拟成像试验的计算体模中起着关键作用。然而，基于计算机断层扫描生成解剖学上合理的器官表面重建，对人体许多结构仍具挑战性，这一挑战在大肠中尤为显著。本研究利用几何深度学习与去噪扩散概率模型的最新进展，对大肠分割结果进行精化。我们首先将器官表示为从三维分割掩模表面采样的点云，随后采用层次变分自编码器获取器官形状的全局与局部潜在表征。在层次潜在空间中训练两个条件去噪扩散模型以执行形状精化。为进一步提升方法性能，我们引入先进表面重建模型，从完备点云生成光滑网格。实验结果表明，该方法能有效捕捉器官形状的全局分布与精细细节。相较于初始分割，完整精化流水线在表面表征方面表现出显著提升：Chamfer距离降低70%，Hausdorff距离降低32%，推土机距离降低6%。通过融合几何深度学习、去噪扩散模型与先进表面重建技术，本方法为精确建模大肠表面提供了可行方案，并可简便推广至其他解剖结构。