Fast assessment of the composition of amyloid fibril samples from cryo-EM data poses a serious challenge to existing image analysis tools. We develop a method for automated segmentation of single fibrils requiring only little user input during the training process. This is achieved by combining a binary segmentation based on a convolutional neural network with preprocessing steps to allow for easy manual generation of training data. Subsequent skeletonization turns the binary segmentation into a single-object segmentation. Then, we compute properties of shape and texture of each segmented fibril, including an estimation of the fibril width. We discuss the composition of the sample based on the distributions of these computed properties and outline how a classification of fibril morphologies might be performed using these properties.

中文翻译：

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冷冻电镜图像数据中淀粉样原纤维的分割和形态分析

从冷冻电镜数据中快速评估淀粉样原纤维样本的组成对现有图像分析工具提出了严峻挑战。我们开发了一种自动分割单纤维的方法，在训练过程中只需要很少的用户输入。这是通过将基于卷积神经网络的二进制分割与预处理步骤相结合来实现的，以便轻松地手动生成训练数据。随后的骨架化将二元分割转变为单对象分割。然后，我们计算每个分段原纤维的形状和纹理属性，包括原纤维宽度的估计。我们根据这些计算属性的分布讨论了样本的组成，并概述了如何使用这些属性对原纤维形态进行分类。