Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for
evaluating medical imaging technologies. Computational phantoms, which mimic
real patient anatomy and physiology, play a central role in VIT. However, the
current libraries of computational phantoms face limitations, particularly in
terms of sample size and diversity. Insufficient representation of the
population hampers accurate assessment of imaging technologies across different
patient groups. Traditionally, phantoms were created by manual segmentation,
which is a laborious and time-consuming task, impeding the expansion of phantom
libraries. This study presents a framework for realistic computational phantom
modeling using a suite of four deep learning segmentation models, followed by
three forms of automated organ segmentation quality control. Over 2500
computational phantoms with up to 140 structures illustrating a sophisticated
approach to detailed anatomical modeling are released. Phantoms are available
in both voxelized and surface mesh formats. The framework is aggregated with an
in-house CT scanner simulator to produce realistic CT images. The framework can
potentially advance virtual imaging trials, facilitating comprehensive and
reliable evaluations of medical imaging technologies. Phantoms may be requested
at this https URL, code, model weights, and sample CT images
are available at this https URL

通过深度学习分割模型和自动器官分割控制的三种形式，释放出 2500 多个计算幻影，形成了一种精细解剖建模的复杂方法，有望推进虚拟成像试验，促进医学成像技术的全面可靠评估。

XCAT-2.0: CT 扫描衍生的个性化数字模型综合库

XCAT-2.0: A Comprehensive Library of Personalized Digital Twins Derived  from CT Scans

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.

准确建模人体器官在构建计算仿像试验中起着关键作用，但对人体内许多结构进行 CT 扫描生成解剖合理的器官表面重建依然具有挑战性。本研究利用几何深度学习和去噪扩散概率模型的最新进展来改进大肠的分割结果，通过将器官表示为从 3D 分割掩模表面采样的点云，然后使用分层变分自动编码器获得器官形状的全局和局部潜在表示，通过在分层潜在空间中训练两个条件去噪扩散模型来进行形状细化。通过使用先进的表面重建模型，我们可以从获取的完整点云生成平滑的网格，实验结果表明了我们方法捕捉到器官形状的全局分布和细节的有效性。与初始分割相比，我们的完整细化流程在表面表示方面展示出显著的提升，Chamfer 距离减少了 70％，Hausdorff 距离减少了 32％，Earth Mover 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有希望的解决方案，也可以轻松扩展到其他解剖结构。