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.
Original language | English |
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Article number | 2 |
Journal | Journal of Mathematics in Industry |
Volume | 13 |
Issue number | 1 |
DOIs | |
Publication status | Published - Dec 2023 |
Externally published | Yes |
Keywords
- Amyloid fibril
- Convolutional neural network
- Cross-over distance
- Cryo-EM image data
- Fibril width
- Single-object segmentation
ASJC Scopus subject areas
- Applied Mathematics