Detecting crossovers in cryo‐electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a time‐consuming procedure. With the rise of deep learning in computer vision tasks, the automation of such problems has become more and more applicable. However, because of insufficient quality of raw data and missing labels, neural networks alone cannot be applied successfully to target the given problem. Thus, we propose an approach combining conventional computer vision techniques and deep learning to automatically detect fibril crossovers in two‐dimensional cryo‐electron microscopy image data and apply it to murine amyloid protein A fibrils, where we first use direct image processing methods to simplify the image data such that a convolutional neural network can be applied to the remaining segmentation problem.

中文翻译：

---

利用机器学习自动识别鼠类淀粉样蛋白 A 原纤维的冷冻 EM 图像中的交叉

在蛋白质原纤维的冷冻电子显微镜图像中检测交叉是确定样品形态组成的重要一步。目前，交叉位置是手工挑选的，这会引入错误并且是一个耗时的过程。随着深度学习在计算机视觉任务中的兴起，此类问题的自动化变得越来越适用。然而，由于原始数据质量不足和标签缺失，单独使用神经网络无法成功解决给定的问题。因此，我们提出了一种结合传统计算机视觉技术和深度学习的方法来自动检测二维冷冻电子显微镜图像数据中的原纤维交叉并将其应用于鼠类淀粉样蛋白 A 原纤维，