With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to electron radiation. Random sparse scanning can be used in the combination with image reconstruction techniques to reduce the acquisition time or electron dose in scanning electron microscopy. In this study, we demonstrate a workflow that includes data acquisition on a scanning electron microscope, followed by a sparse image reconstruction based on compressive sensing or alternatively using neural networks. Neuron structures are automatically segmented from the reconstructed images using deep learning techniques. We show that the average dwell time per pixel can be reduced by a factor of 2–3, thereby providing a real-life confirmation of previous results on simulated data in one of the key segmentation applications in connectomics and thus demonstrating the feasibility and benefit of random sparse scanning techniques for a specific real-world scenario.

中文翻译：

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稀疏扫描电子显微镜数据采集和深度神经网络用于连接组学中的自动分割

随着三维和超大视场成像的重要性日益增加，采集时间成为严重的瓶颈。此外，在对电子辐射敏感的生物组织等材料进行成像时，减少剂量也很重要。随机稀疏扫描可以与图像重建技术结合使用，以减少扫描电子显微镜中的采集时间或电子剂量。在这项研究中，我们展示了一个工作流程，包括在扫描电子显微镜上采集数据，然后基于压缩传感或使用神经网络进行稀疏图像重建。使用深度学习技术从重建的图像中自动分割神经元结构。我们表明，每个像素的平均停留时间可以减少 2-3 倍，