SmartEM: machine learning-guided electron microscopy

Yaron Meirovitch

doi:10.1038/s41592-025-02929-3

2025-12-29

PRE

Aravinthan D. T. Samuel *

Nir N Shavit *

DOI：10.1038/s41592-025-02929-3

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连接组学提供纳米分辨率、突触级别的神经回路图谱，以理解大脑活动和行为。然而，很少有研究者能够接触生成全脑乃至整个回路重建所需数据所必需的高通量电子显微镜。迄今为止，机器学习方法已在电子显微镜（EM）图像采集后用于加速和改进神经元分割、突触重建及其他数据分析。随着EM图像处理计算能力的持续改进，获取EM图像将成为自动化连接组学中的速率限制步骤。在此，为加快EM成像，我们将机器学习整合到单束扫描电子显微镜的实时图像采集中。这种SmartEM方法使电子显微镜能够执行数据感知的样本成像。SmartEM通过为每个感兴趣区域分配适当的成像时间来节省时间——首先快速扫描所有像素，然后仅以更慢的速度重扫需要更高质量信号的小子区域。我们证明，在秀丽隐杆线虫、小鼠和人脑样本中使用商业单束SEM时，SmartEM可使连接组学样本的图像采集时间最多加速约7倍。我们将此快速成像方法应用于重建小鼠部分大脑皮层，其准确度与传统电子显微镜相当。SmartEM是基于电子显微镜的连接组学数据采集的“智能”流水线。为高效成像大数据集，该方法涉及在短像素驻留时间下成像，并识别问题区域，然后以更长的驻留时间（因此质量更高）对其进行成像。

Keyword:

SmartEM

machine learning

electron microscopy

connectomics

image acquisition

期刊

Nature Methods

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32.1

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7.2K

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thermo fisher scientific

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