Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States.
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
Front Neural Circuits. 2022 Nov 25;16:977700. doi: 10.3389/fncir.2022.977700. eCollection 2022.
Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.
脑组织的三维电子显微镜图像及其密集分割现在已经达到了 petascale 级，并且还在不断增长。这些体数据集需要大量生成密集分割后的神经元骨架、多分辨率网格、用于可视化和分析的图像层次结构（针对两种模态），以及用于管理大量数据的工具。然而，目前缺少用于大规模网格处理、骨架化和数据管理的开源工具。Igneous 是一个基于 Python 的分布式计算框架，它使用云或集群计算来实现经济高效的网格处理、骨架化、图像层次结构创建和数据管理，并且已经证明可以水平扩展。我们简述了 Igneous 的计算框架，展示了如何使用它，并对其性能和数据存储进行了特征描述。
