Microvascular networks are challenging to model because these structures are currently near the diffraction limit for most advanced three-dimensional imaging modalities, including confocal and light sheet microscopy. This makes semantic segmentation difficult, because individual components of these networks fluctuate within the confines of individual pixels. Level set methods are ideally suited to solve this problem by providing surface and topological constraints on the resulting model, however these active contour techniques are extremely time intensive and impractical for terabyte-scale images. We propose a reformulation and implementation of the region-scalable fitting (RSF) level set model that makes it amenable to three-dimensional evaluation using both single-instruction multiple data (SIMD) and single-program multiple-data (SPMD) parallel processing. This enables evaluation of the level set equation on independent regions of the data set using graphics processing units (GPUs), making large-scale segmentation of high-resolution networks practical and inexpensive. We tested this 3D parallel RSF approach on multiple data sets acquired using state-of-the-art imaging techniques to acquire microvascular data, including micro-CT, light sheet fluorescence microscopy (LSFM) and milling microscopy. To assess the performance and accuracy of the RSF model, we conducted a Monte-Carlo-based validation technique to compare results to other segmentation methods. We also provide a rigorous profiling to show the gains in processing speed leveraging parallel hardware. This study showcases the practical application of the RSF model, emphasizing its utility in the challenging domain of segmenting large-scale high-topology network structures with a particular focus on building microvascular models.

翻译：微血管网络因目前大多数先进三维成像技术（包括共聚焦显微镜和光片显微镜）的衍射极限限制而难以建模。这使得语义分割变得困难，因为网络中的单个组件会在单个像素范围内波动。水平集方法通过为模型提供表面和拓扑约束，非常适合解决这一问题，然而这类主动轮廓技术极其耗时，且不适用于TB级图像。我们提出了一种区域可扩展拟合（RSF）水平集模型的重新公式化与实现，使其能够通过单指令多数据（SIMD）和单程序多数据（SPMD）并行处理进行三维评估。这允许利用图形处理单元（GPU）对数据集的独立区域进行水平集方程的评估，从而使高分辨率网络的大规模分割变得实用且经济。我们使用最先进的成像技术（包括显微CT、光片荧光显微镜和磨削显微镜）获取的多个微血管数据集，测试了这种三维并行RSF方法。为评估RSF模型的性能与精度，我们采用基于蒙特卡洛的验证技术将其结果与其他分割方法进行比较。我们还提供了严格的性能分析，以展示利用并行硬件所带来的处理速度提升。本研究展示了RSF模型的实际应用，强调了其在分割大规模高拓扑网络结构这一挑战性领域中的效用，尤其侧重于构建微血管模型。