面向生物医学仿真的图像到网格转换
Image-To-Mesh Conversion for Biomedical Simulations
AI总结：
针对生物医学场景中三维医学图像转网格需兼顾质量与保真度的难题，提出CBC3D方法，通过自适应BCC网格离散、混合单元转换及能量最小化变形实现高质量转换，性能优于现有主流方法。
摘要：
将三维医学图像转换为同时满足预测仿真和图像引导手术程序的质量与保真度约束的3D网格，仍是一个关键问题。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由高质量单元构成的自适应体心立方（Body-Centered Cubic，BCC）网格，对分割后的图像进行离散化。接着，将四面体网格转换为由四面体、五面体和六面体组成的混合单元网格，以在保持单元质量的同时减少单元数量。最后，将网格表面变形至其对应的物理图像边界，提升网格的保真度。该变形方案基于ITK开源库构建，以能量最小化概念为基础，依赖多材料基于点的配准技术。它利用非连通模式隐式控制配准所需提取的特征点数量，从而调整实现的网格保真度与变形速度之间的权衡。我们将CBC3D与四种来自工业界和学术界的广泛使用且为顶尖水平的自研图像到网格转换方法进行了对比。结果表明，CBC3D生成的网格（i）实现了高保真度，（ii）将单元数量维持在合理的较低水平，（iii）展现出良好的单元质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
