Combining scanning electron microscopy with serial slicing by a focused ion beam yields spatial image data of materials structures at the nanometer scale. However, the depth of field of the scanning electron microscopic images causes unwanted effects when highly porous structures are imaged. Proper spatial reconstruction of such porous structures from the stack of microscopic images is a tough and in general yet unsolved segmentation problem. Recently, machine learning methods have proven to yield solutions to a variety of image segmentation problems. However, their use is hindered by the need of large amounts of annotated data in the training phase. Here, we therefore replace annotated real image data by simulated image stacks of synthetic structures – realizations of stochastic germ–grain models and random packings. This strategy yields the annotations for free, but shifts the effort to choosing appropriate stochastic geometry models and generating sufficiently realistic scanning electron microscopic images.

中文翻译：

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使用在合成图像上训练的深度神经网络从 FIB-SEM 重建高度多孔结构

将扫描电子显微镜与聚焦离子束的连续切片相结合，可以产生纳米级材料结构的空间图像数据。然而，当对高度多孔的结构成像时，扫描电子显微图像的景深会导致不需要的影响。从显微图像堆栈中对这种多孔结构进行适当的空间重建是一个艰难且通常尚未解决的分割问题。最近，机器学习方法已被证明可以解决各种图像分割问题。然而，它们的使用受到训练阶段需要大量注释数据的阻碍。因此，我们在这里用合成结构的模拟图像堆栈替换带注释的真实图像数据——随机胚芽模型和随机包装的实现。