Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data

Abstract

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.

Advancing electron microscopy using deep learning

“…To illustrate the potential impact, DL algorithms can be trained to segment TEM images and identify regions of interest, such as cells, nanoparticles, or defects [61][62][63][64]. Image segmentation is the process of separating an image into different regions or objects [65][66][67][68][69][70], which can be useful for analyzing the distribution and characteristics of different features in a sample [71]. This includes SEM [69] and x-ray tomographic [72] microscopy.…”