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

Journal of Mathematics in Industry

Research

Matthias Schmidt¹  Akanksha Bansal¹  Peter Benedikt Pfeiffer¹  Marcus Fändrich¹  Matthias Weber²  Matthias Neumann²  Volker Schmidt² 

[1] Institute of Protein Biochemistry, Ulm University, Ulm, Germany;Institute of Stochastics, Ulm University, Ulm, Germany;

关键词: Cryo-EM image data;    Amyloid fibril;    Cross-over distance;    Fibril width;    Single-object segmentation;    Convolutional neural network;   

DOI  :  10.1186/s13362-023-00131-8

 received in 2022-06-24, accepted in 2023-01-19,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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