Isocontouring is one of the most widely used visualization techniques. However, many popular contouring algorithms were created prior to the advent of ubiquitous parallel approaches, such as multi-core, shared memory computing systems. With increasing data sizes and computational loads, it is essential to reimagine such algorithms to leverage the increased computing capabilities available today. To this end we have redesigned the SurfaceNets algorithm, a powerful technique which is often employed to isocontour non-continuous, discrete, volumetric scalar fields such as segmentation label maps. Label maps are ubiquitous to medical computing and biological analysis, used in applications ranging from anatomical atlas creation to brain connectomics. This novel Parallel SurfaceNets algorithm has been redesigned using concepts from the high-performance Flying Edges continuous isocontouring algorrithm. It consists of two basic steps, surface extraction followed by constrained smoothing, parallelized over volume edges and employing a double-buffering smoothing approach to guarantee determinism. The algorithm can extract and smooth multiple segmented objects in a single execution, producing a polygonal (triangular/quadrilateral) mesh with points and polygons fully shared between neighboring objects. Performance is typically one to two orders of magnitude faster than the current sequential algorithms for discrete isosurface extraction on small core-count commodity CPU hardware. We demonstrate the effectiveness of the algorithm on five different datasets including human torso and brain atlases, mouse brain segmentation, and electron microscopy connectomics. The software is currently available under a permissive, open source license in the VTK visualization system.

翻译：等值线提取是最广泛使用的可视化技术之一。然而，许多流行的等值线算法诞生于并行方法（如多核共享内存计算系统）普及之前。随着数据规模和计算负载的不断增加，有必要重新构想此类算法以利用当今日益增强的计算能力。为此，我们重新设计了SurfaceNets算法——该技术常用于对分割标签图等非连续、离散体标量场进行等值线提取。标签图在医学计算和生物分析中无处不在，应用于从解剖图谱构建到脑连接组学等多个领域。这种新型并行SurfaceNets算法借鉴了高性能FlyingEdges连续等值线提取算法的概念重新设计。该算法包含两个基本步骤：表面提取与约束平滑，通过体边缘并行化实现，并采用双缓冲平滑方法确保确定性。该算法可在单次执行中提取并平滑多个分割对象，生成相邻对象间完全共享顶点和多边形的多边形（三角形/四边形）网格。在低核心数商用CPU硬件上，其离散等值面提取性能通常比现有串行算法快一到两个数量级。我们在五组不同数据集（包括人体躯干和脑图谱、小鼠脑分割以及电子显微镜连接组学）上验证了该算法的有效性。该软件目前已在VTK可视化系统中以宽松开源许可协议提供。