Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic (BCC) mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed-element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh's fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes (i) achieve high fidelity, (ii) keep the element count reasonably low, and (iii) exhibit good element quality.

翻译：将三维医学图像转换为满足预测性仿真和图像引导外科手术中质量与保真度约束的三维网格，仍是一个关键难题。本文提出了一种名为CBC3D的图像到网格转换方法。该方法首先通过生成自适应体心立方(BCC)高质量单元网格对分割图像进行离散化，然后将四面体网格转换为包含四面体、五面体和六面体的混合单元网格，以在保持质量的同时减少单元数量。最后，通过将网格表面变形至对应的物理图像边界来提升网格保真度。该变形方案基于ITK开源库，采用能量最小化概念，依托多材料点配准技术，借助非连接模式隐式控制配准所需特征点的提取数量，从而平衡网格保真度与变形速度。我们将CBC3D与工业界和学术界四种广泛使用且代表最新水平的图像到网格转换方法进行对比。结果表明：CBC3D生成的网格具有高保真度、合理的低单元数量以及良好的单元质量。