Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation

TL;DR

This paper introduces CLAP, a novel diffusion-based method that significantly improves 3D large intestine shape modeling from point clouds, aiding in the creation of accurate digital phantoms for virtual imaging.

Contribution

The paper presents a new hierarchical variational autoencoder combined with conditional diffusion models for detailed 3D organ shape refinement, advancing digital phantom generation.

Findings

Reduced shape modeling errors by 26% in Chamfer distance.

Decreased Hausdorff distance by 36% compared to initial shapes.

Demonstrated robustness and extensibility for various anatomical structures.

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.