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

Title: 大肠三维形状细化用于数字幻影生成的点扩散模型

Abstract: Accurate 3D modeling of human organs is critical for constructing digital phantoms in virtual imaging trials. However, organs such as the large intestine remain particularly challenging due to their complex geometry and shape variability. We propose CLAP, a novel Conditional LAtent Point-diffusion model that combines geometric deep learning with denoising diffusion models to enhance 3D representations of the large intestine. Given point clouds sampled from segmentation masks, we employ a hierarchical variational autoencoder to learn both global and local latent shape representations. Two conditional diffusion models operate within this latent space to refine the organ shape. A pretrained surface reconstruction model is then used to convert the refined point clouds into meshes. CLAP achieves substantial improvements in shape modeling accuracy, reducing Chamfer distance by 26% and Hausdorff distance by 36% relative to the initial suboptimal shapes. This approach offers a robust and extensible solution for high-fidelity organ modeling, with potential applicability to a wide range of anatomical structures. Show Chinese abstract

Abstract: 准确的人体器官三维建模对于构建虚拟成像试验中的数字幻影至关重要。 然而，由于其复杂的几何形状和形态变异性，如大肠等器官仍然特别具有挑战性。 我们提出了CLAP，一种新颖的条件隐空间点扩散模型，它结合了几何深度学习与去噪扩散模型，以增强大肠的三维表示。 给定点云从分割掩膜中采样，我们采用分层变分自编码器来学习全局和局部隐形状表示。 两个条件扩散模型在此隐空间内运行以细化器官形状。 然后使用预训练的表面重建模型将细化后的点云转换为网格。 CLAP在形状建模精度方面实现了显著提升，相对于初始次优形状，减少了26%的Chamfer距离和36%的Hausdorff距离。 这种方法为高保真器官建模提供了一个稳健且可扩展的解决方案，具有广泛应用于各种解剖结构的潜力。