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 combi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-02-01
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| Series: | Journal of Mathematics in Industry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13362-023-00131-8 |
| _version_ | 1811166034043863040 |
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| author | Matthias Weber Matthias Neumann Matthias Schmidt Peter Benedikt Pfeiffer Akanksha Bansal Marcus Fändrich Volker Schmidt |
| author_facet | Matthias Weber Matthias Neumann Matthias Schmidt Peter Benedikt Pfeiffer Akanksha Bansal Marcus Fändrich Volker Schmidt |
| author_sort | Matthias Weber |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T15:46:56Z |
| format | Article |
| id | doaj.art-f34184b1d8f04664acd7bbb827ec4d4c |
| institution | Directory Open Access Journal |
| issn | 2190-5983 |
| language | English |
| last_indexed | 2024-04-10T15:46:56Z |
| publishDate | 2023-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Mathematics in Industry |
| spelling | doaj.art-f34184b1d8f04664acd7bbb827ec4d4c2023-02-12T12:06:23ZengSpringerOpenJournal of Mathematics in Industry2190-59832023-02-0113111110.1186/s13362-023-00131-8Segmentation and morphological analysis of amyloid fibrils from cryo-EM image dataMatthias Weber0Matthias Neumann1Matthias Schmidt2Peter Benedikt Pfeiffer3Akanksha Bansal4Marcus Fändrich5Volker Schmidt6Institute of Stochastics, Ulm UniversityInstitute of Stochastics, Ulm UniversityInstitute of Protein Biochemistry, Ulm UniversityInstitute of Protein Biochemistry, Ulm UniversityInstitute of Protein Biochemistry, Ulm UniversityInstitute of Protein Biochemistry, Ulm UniversityInstitute of Stochastics, Ulm UniversityAbstract 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.https://doi.org/10.1186/s13362-023-00131-8Cryo-EM image dataAmyloid fibrilCross-over distanceFibril widthSingle-object segmentationConvolutional neural network |
| spellingShingle | Matthias Weber Matthias Neumann Matthias Schmidt Peter Benedikt Pfeiffer Akanksha Bansal Marcus Fändrich Volker Schmidt Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data Journal of Mathematics in Industry Cryo-EM image data Amyloid fibril Cross-over distance Fibril width Single-object segmentation Convolutional neural network |
| title | Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data |
| title_full | Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data |
| title_fullStr | Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data |
| title_full_unstemmed | Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data |
| title_short | Segmentation and morphological analysis of amyloid fibrils from cryo-EM image data |
| title_sort | segmentation and morphological analysis of amyloid fibrils from cryo em image data |
| topic | Cryo-EM image data Amyloid fibril Cross-over distance Fibril width Single-object segmentation Convolutional neural network |
| url | https://doi.org/10.1186/s13362-023-00131-8 |
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