  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.
