Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations
G-ratio is crucial for understanding the nervous system’s health and function as it measures the relative myelin thickness around an axon. However, manual measurement is biased and variable, emphasizing the need for an automated and standardized technique. Although deep learning holds promise, curre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Neuroanatomy |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnana.2023.1260186/full |
| _version_ | 1797491316514357248 |
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| author | Bart R. Thomson Louise Françoise Martin Paul L. Schmidle Hannah Schlierbach Anne Schänzer Henning Richter |
| author_facet | Bart R. Thomson Louise Françoise Martin Paul L. Schmidle Hannah Schlierbach Anne Schänzer Henning Richter |
| author_sort | Bart R. Thomson |
| collection | DOAJ |
| description | G-ratio is crucial for understanding the nervous system’s health and function as it measures the relative myelin thickness around an axon. However, manual measurement is biased and variable, emphasizing the need for an automated and standardized technique. Although deep learning holds promise, current implementations lack clinical relevance and generalizability. This study aimed to develop an automated pipeline for selecting nerve fibers and calculating relevant g-ratio using quality parameters in optical microscopy. Histological sections from the sciatic nerves of 16 female mice were prepared and stained with either p-phenylenediamine (PPD) or toluidine blue (TB). A custom UNet model was trained on a mix of both types of staining to segment the sections based on 7,694 manually delineated nerve fibers. Post-processing excluded non-relevant nerves. Axon diameter, myelin thickness, and g-ratio were computed from the segmentation results and its reliability was assessed using the intraclass correlation coefficient (ICC). Validation was performed on adjacent cuts of the same nerve. Then, morphometrical analyses of both staining techniques were performed. High agreement with the ground truth was shown by the model, with dice scores of 0.86 (axon) and 0.80 (myelin) and pixel-wise accuracy of 0.98 (axon) and 0.94 (myelin). Good inter-device reliability was observed with ICC at 0.87 (g-ratio) and 0.83 (myelin thickness), and an excellent ICC of 0.99 for axon diameter. Although axon diameter significantly differed from the ground truth (p = 0.006), g-ratio (p = 0.098) and myelin thickness (p = 0.877) showed no significant differences. No statistical differences in morphological parameters (g-ratio, myelin thickness, and axon diameter) were found in adjacent cuts of the same nerve (ANOVA p-values: 0.34, 0.34, and 0.39, respectively). Comparing all animals, staining techniques yielded significant differences in mean g-ratio (PPD: 0.48 ± 0.04, TB: 0.50 ± 0.04), myelin thickness (PPD: 0.83 ± 0.28 μm, TB: 0.60 ± 0.20 μm), and axon diameter (PPD: 1.80 ± 0.63 μm, TB: 1.78 ± 0.63 μm). The proposed pipeline automatically selects relevant nerve fibers for g-ratio calculation in optical microscopy. This provides a reliable measurement method and serves as a potential pre-selection approach for large datasets in the context of healthy tissue. It remains to be demonstrated whether this method is applicable to measure g-ratio related with neurological disorders by comparing healthy and pathological tissue. Additionally, our findings emphasize the need for careful interpretation of inter-staining morphological parameters. |
| first_indexed | 2024-03-10T00:46:43Z |
| format | Article |
| id | doaj.art-0cc3a92cb0614192a7242ae77534900e |
| institution | Directory Open Access Journal |
| issn | 1662-5129 |
| language | English |
| last_indexed | 2024-03-10T00:46:43Z |
| publishDate | 2023-11-01 |
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| series | Frontiers in Neuroanatomy |
| spelling | doaj.art-0cc3a92cb0614192a7242ae77534900e2023-11-23T14:57:49ZengFrontiers Media S.A.Frontiers in Neuroanatomy1662-51292023-11-011710.3389/fnana.2023.12601861260186Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variationsBart R. Thomson0Louise Françoise Martin1Paul L. Schmidle2Hannah Schlierbach3Anne Schänzer4Henning Richter5Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, SwitzerlandInstitute of Laboratory Animal Science, Vetsuisse Faculty, University of Zurich, Zurich, SwitzerlandDepartment of Dermatology, University Hospital Muenster, Muenster, GermanyInstitute of Neuropathology, Justus Liebig University Giessen, Giessen, GermanyInstitute of Neuropathology, Justus Liebig University Giessen, Giessen, GermanyDiagnostic Imaging Research Unit (DIRU), Clinic for Diagnostic Imaging, Vetsuisse Faculty, University of Zurich, Zurich, SwitzerlandG-ratio is crucial for understanding the nervous system’s health and function as it measures the relative myelin thickness around an axon. However, manual measurement is biased and variable, emphasizing the need for an automated and standardized technique. Although deep learning holds promise, current implementations lack clinical relevance and generalizability. This study aimed to develop an automated pipeline for selecting nerve fibers and calculating relevant g-ratio using quality parameters in optical microscopy. Histological sections from the sciatic nerves of 16 female mice were prepared and stained with either p-phenylenediamine (PPD) or toluidine blue (TB). A custom UNet model was trained on a mix of both types of staining to segment the sections based on 7,694 manually delineated nerve fibers. Post-processing excluded non-relevant nerves. Axon diameter, myelin thickness, and g-ratio were computed from the segmentation results and its reliability was assessed using the intraclass correlation coefficient (ICC). Validation was performed on adjacent cuts of the same nerve. Then, morphometrical analyses of both staining techniques were performed. High agreement with the ground truth was shown by the model, with dice scores of 0.86 (axon) and 0.80 (myelin) and pixel-wise accuracy of 0.98 (axon) and 0.94 (myelin). Good inter-device reliability was observed with ICC at 0.87 (g-ratio) and 0.83 (myelin thickness), and an excellent ICC of 0.99 for axon diameter. Although axon diameter significantly differed from the ground truth (p = 0.006), g-ratio (p = 0.098) and myelin thickness (p = 0.877) showed no significant differences. No statistical differences in morphological parameters (g-ratio, myelin thickness, and axon diameter) were found in adjacent cuts of the same nerve (ANOVA p-values: 0.34, 0.34, and 0.39, respectively). Comparing all animals, staining techniques yielded significant differences in mean g-ratio (PPD: 0.48 ± 0.04, TB: 0.50 ± 0.04), myelin thickness (PPD: 0.83 ± 0.28 μm, TB: 0.60 ± 0.20 μm), and axon diameter (PPD: 1.80 ± 0.63 μm, TB: 1.78 ± 0.63 μm). The proposed pipeline automatically selects relevant nerve fibers for g-ratio calculation in optical microscopy. This provides a reliable measurement method and serves as a potential pre-selection approach for large datasets in the context of healthy tissue. It remains to be demonstrated whether this method is applicable to measure g-ratio related with neurological disorders by comparing healthy and pathological tissue. Additionally, our findings emphasize the need for careful interpretation of inter-staining morphological parameters.https://www.frontiersin.org/articles/10.3389/fnana.2023.1260186/fullg-ratiosciatic nervemiceoptical microscopydeep learningsegmentation |
| spellingShingle | Bart R. Thomson Louise Françoise Martin Paul L. Schmidle Hannah Schlierbach Anne Schänzer Henning Richter Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations Frontiers in Neuroanatomy g-ratio sciatic nerve mice optical microscopy deep learning segmentation |
| title | Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations |
| title_full | Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations |
| title_fullStr | Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations |
| title_full_unstemmed | Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations |
| title_short | Automated pipeline for nerve fiber selection and g-ratio calculation in optical microscopy: exploring staining protocol variations |
| title_sort | automated pipeline for nerve fiber selection and g ratio calculation in optical microscopy exploring staining protocol variations |
| topic | g-ratio sciatic nerve mice optical microscopy deep learning segmentation |
| url | https://www.frontiersin.org/articles/10.3389/fnana.2023.1260186/full |
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