Pons J P, Ségonne E, Boissonnat J D, Rineau L, Yvinec M, Keriven R
WILLOW, ENPC / ENS / INRIA, Paris, France.
Inf Process Med Imaging. 2007;20:198-210. doi: 10.1007/978-3-540-73273-0_17.
In this paper, we extend some recent provably correct Delaunay-based meshing algorithms to the case of multi-label partitions, so that they can be applied to the generation of high-quality geometric models from labeled medical datasets. Our approach enforces watertight surface meshes free of self-intersections, and outputs surface and volume meshes of the different tissues which are consistent with each other, including at multiple junctions. Moreover, the abstraction of the tissue partition into an oracle that, given a point in space, answers which tissue it belongs to, makes our approach applicable to virtually any combination of data sources. Finally, our approach offers extensive control over the size and shape of mesh elements, through customizable quality criteria on triangular facets and on tetrahedra, which can be tuned independently for the different anatomical structures. Our numerical experiments demonstrate the effectiveness and flexibility of our approach for generating high-quality surface and volume meshes from real multi-label medical datasets.
在本文中，我们将一些最近已证明正确的基于德劳内三角剖分的网格划分算法扩展到多标签分区的情况，以便它们能够应用于从带标签的医学数据集中生成高质量的几何模型。我们的方法生成无自相交的封闭表面网格，并输出相互一致的不同组织的表面和体积网格，包括在多个交界处。此外，将组织分区抽象为一个神谕，给定空间中的一个点，能回答该点所属的组织，这使得我们的方法几乎适用于任何数据源组合。最后，我们的方法通过对三角形面和四面体的可定制质量标准，对网格单元的大小和形状提供了广泛的控制，这些标准可以针对不同的解剖结构独立调整。我们的数值实验证明了我们的方法从真实的多标签医学数据集中生成高质量表面和体积网格的有效性和灵活性。
