Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data
Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data
复制标题
利用 Petavoxel 电子显微镜数据进行自动神经重建
DOI：
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发表时间：
2016
影响因子：
2.8
通讯作者：
H. Pfister
中科院分区：
文献类型：
--
作者：
Adi Suissa;D. Haehn;Seymour Knowles;V. Kaynig;T. Jones;A. Wilson;R. Schalek;J. Lichtman;H. Pfister
连接组学是对大脑中神经元及其突触的致密结构的研究，为大脑结构与功能之间的关系提供了新的见解。电子显微镜的最新进展使神经组织的高分辨率成像（每像素4 nm）能够在一天内以大约10万亿像素的速度进行，使神经科学家能够在合理的时间内捕获大块神经组织。大量的数据需要新的基于计算机视觉的算法和可扩展的软件框架来处理这些数据。我们描述了RhoANA [1]，我们的密集自动神经注释框架，我们开发了该框架，以便自动对齐，分割和重建1 mm 3脑组织（约2 peta-pixel）。
Connectomics is the study of the dense structure of the neurons in the brain and their synapses, providing new insights into the relation between brain’s structure and its function. Recent advances in Electron Microscopy enable high-resolution imaging (4nm per pixel) of neural tissue at a rate of roughly 10 terapixels in a single day, allowing neuroscientists to capture large blocks of neural tissue in a reasonable amount of time. The large amounts of data require novel computer vision based algorithms and scalable software frameworks to process this data. We describe RhoANA [1], our dense Automatic Neural Annotation framework, which we have developed in order to automatically align, segment and reconstruct a 1mm 3 brain tissue (~2 peta-pixels).
影响因子：
48
作者：
Saalfeld, Stephan;Fetter, Richard;Tomancak, Pavel
通讯作者：
Tomancak, Pavel