Myelin detection in fluorescence microscopy images using machine learning
Copyright © 2020 Elsevier B.V. All rights reserved..
BACKGROUND: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens.
NEW METHOD: In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total.
RESULTS: Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84±0.09% and 98.46±0.11% were achieved by Boosted Trees and customized-CNN, respectively.
COMPARISON WITH EXISTING METHODS: Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images.
CONCLUSIONS: Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:346 |
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| Enthalten in: |
Journal of neuroscience methods - 346(2020) vom: 01. Dez., Seite 108946 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Çimen Yetiş, Sibel [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 21.06.2021 Date Revised 21.06.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jneumeth.2020.108946 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM315037253 |
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| 500 | |a Date Revised 21.06.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens | ||
| 520 | |a NEW METHOD: In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total | ||
| 520 | |a RESULTS: Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84±0.09% and 98.46±0.11% were achieved by Boosted Trees and customized-CNN, respectively | ||
| 520 | |a COMPARISON WITH EXISTING METHODS: Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images | ||
| 520 | |a CONCLUSIONS: Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Fluorescence image analysis | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Myelin detection | |
| 650 | 4 | |a Myelin quantification | |
| 650 | 4 | |a Supervised learning | |
| 700 | 1 | |a Çapar, Abdulkerim |e verfasserin |4 aut | |
| 700 | 1 | |a Ekinci, Dursun A |e verfasserin |4 aut | |
| 700 | 1 | |a Ayten, Umut E |e verfasserin |4 aut | |
| 700 | 1 | |a Kerman, Bilal E |e verfasserin |4 aut | |
| 700 | 1 | |a Töreyin, B Uğur |e verfasserin |4 aut | |
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