Macke Jakob H, Maack Nina, Gupta Rocky, Denk Winfried, Schölkopf Bernhard, Borst Alexander
Max Planck Institute for Biological Cybernetics, Spemannstrasse 38, D-72076 Tübingen, Germany.
J Neurosci Methods. 2008 Jan 30;167(2):349-57. doi: 10.1016/j.jneumeth.2007.07.021. Epub 2007 Aug 10.
A new technique, "serial block face scanning electron microscopy" (SBFSEM), allows for automatic sectioning and imaging of biological tissue with a scanning electron microscope. Image stacks generated with this technology have a resolution sufficient to distinguish different cellular compartments, including synaptic structures, which should make it possible to obtain detailed anatomical knowledge of complete neuronal circuits. Such an image stack contains several thousands of images and is recorded with a minimal voxel size of 10-20 nm in the x- and y-direction and 30 nm in z-direction. Consequently, a tissue block of 1 mm(3)(the approximate volume of the Calliphora vicina brain) will produce several hundred terabytes of data. Therefore, highly automated 3D reconstruction algorithms are needed. As a first step in this direction we have developed semi-automated segmentation algorithms for a precise contour tracing of cell membranes. These algorithms were embedded into an easy-to-operate user interface, which allows direct 3D observation of the extracted objects during the segmentation of image stacks. Compared to purely manual tracing, processing time is greatly accelerated.
一种新技术“连续块面扫描电子显微镜”（SBFSEM），能够利用扫描电子显微镜对生物组织进行自动切片和成像。用该技术生成的图像堆栈分辨率足以区分不同的细胞区室，包括突触结构，这应该使得获取完整神经元回路的详细解剖学知识成为可能。这样的图像堆栈包含数千张图像，在x和y方向上的最小体素尺寸为10 - 20纳米，在z方向上为30纳米。因此，1立方毫米的组织块（近似丽蝇脑的体积）将产生数百太字节的数据。所以，需要高度自动化的三维重建算法。作为朝这个方向迈出的第一步，我们已经开发了用于细胞膜精确轮廓追踪的半自动分割算法。这些算法被嵌入到一个易于操作的用户界面中，该界面允许在图像堆栈分割过程中对提取的对象进行直接三维观察。与纯手动追踪相比，处理时间大大加快。
