Watkins Paul V, Jelli Eric, Briggman Kevin L
Max Planck Institute for Neurobiology of Behavior-caesar, Bonn, Germany.
Front Neurosci. 2023 Dec 12;17:1281098. doi: 10.3389/fnins.2023.1281098. eCollection 2023.
Serial section multibeam scanning electron microscopy (ssmSEM) is currently among the fastest technologies available for acquiring 3D anatomical data spanning relatively large neural tissue volumes, on the order of 1 mm or larger, at a resolution sufficient to resolve the fine detail of neuronal morphologies and synapses. These petabyte-scale volumes can be analyzed to create connectomes, datasets that contain detailed anatomical information including synaptic connectivity, neuronal morphologies and distributions of cellular organelles. The mSEM acquisition process creates hundreds of millions of individual image tiles for a single cubic-millimeter-sized dataset and these tiles must be aligned to create 3D volumes. Here we introduce , an alignment pipeline that strives for scalability and design simplicity. The pipeline can align petabyte-scale datasets such that they contain smooth transitions as the dataset is navigated in all directions, but critically that does so in a fashion that minimizes the overall magnitude of section distortions relative to the originally acquired micrographs.
连续切片多束扫描电子显微镜（ssmSEM）是目前获取跨越相对较大神经组织体积（约1毫米或更大）的三维解剖数据最快的技术之一，其分辨率足以解析神经元形态和突触的精细细节。这些PB级别的数据体可以进行分析以创建连接组，即包含详细解剖信息（包括突触连接性、神经元形态和细胞器分布）的数据集。对于单个立方毫米大小的数据集，mSEM采集过程会生成数亿个单独的图像切片，并且必须对齐这些切片以创建三维数据体。在这里，我们介绍一种力求可扩展性和设计简单性的对齐流程。该流程可以对齐PB级别的数据集，使得在数据集向各个方向导航时包含平滑过渡，但关键的是，这样做的方式能将相对于原始采集的显微照片的切片失真的总体幅度降至最低。
