Title: Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation Show Chinese title

Title: 基于点扩散模型的结肠三维形状优化用于数字幻影生成

Abstract: 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. Show Chinese abstract

Abstract: 精确的人体器官三维建模在构建虚拟成像试验的计算幻影中起着关键作用。 然而，从计算机断层扫描中生成解剖学上合理的器官表面重建对于人体中的许多结构仍然具有挑战性。 这种挑战在处理大肠时尤为明显。 在本研究中，我们利用几何深度学习和去噪扩散概率模型的最新进展来优化大肠的分割结果。 我们首先将器官表示为从3D分割掩膜表面采样的点云。 随后，我们采用分层变分自编码器来获得器官形状的全局和局部潜在表示。 我们在分层潜在空间中训练两个条件去噪扩散模型以执行形状优化。 为了进一步增强我们的方法，我们结合了一个最先进的表面重建模型，使我们能够从获得的完整点云生成平滑的网格。 实验结果表明，我们的方法在捕捉器官形状的整体分布和细节方面是有效的。 我们的完整优化流程相比初始分割在表面表示方面表现出显著的改进，将Chamfer距离减少了70%，Hausdorff距离减少了32%，Earth Mover's距离减少了6%。 通过结合几何深度学习、去噪扩散模型和先进的表面重建技术，我们提出的方法为准确建模大肠表面提供了一个有前景的解决方案，并且可以轻松扩展到其他解剖结构。