Contextual High-Throughput 3D Volume Electron Microscopy Data Acquisition Using Artificial Intelligence

doi:10.1093/mam/ozag014

2026-03-13

PRE

Tereza Hurník Konečná

James A J Fitzpatrick

Kirk J. Czymmek *

DOI：10.1093/mam/ozag014

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生成大型和高分辨率的体电子显微镜(vEM)三维(3D)数据集面临的最大挑战之一是采集时间过长。我们开发了一种利用人工智能算法的方法，通过为感兴趣的目标区域创建AI衍生的掩模来提高3D数据集的采集吞吐量，该方法被称为自适应扫描。这使得特定结构能够以高分辨率成像，而周围区域以较低分辨率捕获，无需人为生成或增强任何原始数据。我们证明，这种动态分辨率扫描方法显著缩短了采集时间，具体取决于感兴趣像素的数量，并且与样品无关，能够兼容包括大脑、寄生虫、培养细胞和植物在内的多种生物体和组织。这种多分辨率策略有望通过将时间节省提高至两倍或更多，来增强聚焦离子束扫描电子显微镜和其他形式的vEM，从而更高效、更经济地实现多个和/或更大vEM数据集的常规生成，并允许收集更多数据以增加比较研究的统计功效。

Keyword:

Artificial intelligence

3D volume electron microscopy

Adaptive Scanning

multiresolution imaging

acquisition throughput

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Microscopy and Microanalysis

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donald danforth plant science center

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