PDF(Pubmed)
Abstract：
Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.
摘要：
Connectomics提供基本的纳米分辨率，神经回路的突触水平图，以了解大脑活动和行为。然而,很少有研究人员能够获得为整个电路或大脑重建生成足够数据所必需的高通量电子显微镜。迄今为止,在通过电子显微镜（EM）收集图像之后，已经使用机器学习方法来加速和改善神经元分割，突触重建和其他数据分析。随着处理EM图像的计算改进，获取EM图像现在已成为限速步骤。这里,为了加速EM成像，我们在单束扫描电子显微镜中将机器学习集成到实时图像采集中。这种SmartEM方法允许电子显微镜执行智能，标本的数据感知成像。SmartEM为每个感兴趣区域分配适当的成像时间-快速扫描所有像素，然后更慢地重新扫描需要更高质量信号以实现准确的可分割性的小分区，在明显更少的时间。我们证明了该管道使用商业单光束SEM实现了连接组学的图像采集时间的7倍加速。我们应用SmartEM以与传统显微镜相同的精度重建小鼠皮层的一部分，但时间更短。
