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Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features

Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatica...

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Autores principales: Hohmann, Urszula, Dehghani, Faramarz, Hohmann, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531652/
https://www.ncbi.nlm.nih.gov/pubmed/34690679
http://dx.doi.org/10.3389/fnins.2021.740178
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author Hohmann, Urszula
Dehghani, Faramarz
Hohmann, Tim
author_facet Hohmann, Urszula
Dehghani, Faramarz
Hohmann, Tim
author_sort Hohmann, Urszula
collection PubMed
description Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatically lesioned and subsequently treated with potential neuroprotective agents. Thereby cell death is regularly assessed by measuring the propidium iodide (PI) uptake or counting of PI-positive nuclei. The applied methods have a limited applicability, either in terms of objectivity and time consumption or regarding its applicability. Consequently, new tools for analysis are needed. Here, we present a framework to mimic manual counting using machine learning algorithms as tools for semantic segmentation of PI-positive dead cells in hippocampal slice cultures. Therefore, we trained a support vector machine (SVM) to classify images into either “high” or “low” neuronal damage and used naïve Bayes, discriminant analysis, random forest, and a multilayer perceptron (MLP) as classifiers for segmentation of dead cells. In our final models, pixel-wise accuracies of up to 0.97 were achieved using the MLP classifier. Furthermore, a SVM-based post-processing step was introduced to differentiate between false-positive and false-negative detections using morphological features. As only very few false-positive objects and thus training data remained when using the final model, this approach only mildly improved the results. A final object splitting step using Hough transformations was used to account for overlap, leading to a recall of up to 97.6% of the manually assigned PI-positive dead cells. Taken together, we present an analysis tool that can help to objectively and reproducibly analyze neuronal damage in brain-derived slice cultures, taking advantage of the morphology of pycnotic cells for segmentation, object splitting, and identification of false positives.
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spelling pubmed-85316522021-10-23 Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features Hohmann, Urszula Dehghani, Faramarz Hohmann, Tim Front Neurosci Neuroscience Neuronal damage presents a major health issue necessitating extensive research to identify mechanisms of neuronal cell death and potential therapeutic targets. Commonly used models are slice cultures out of different brain regions extracted from mice or rats, excitotoxically, ischemic, or traumatically lesioned and subsequently treated with potential neuroprotective agents. Thereby cell death is regularly assessed by measuring the propidium iodide (PI) uptake or counting of PI-positive nuclei. The applied methods have a limited applicability, either in terms of objectivity and time consumption or regarding its applicability. Consequently, new tools for analysis are needed. Here, we present a framework to mimic manual counting using machine learning algorithms as tools for semantic segmentation of PI-positive dead cells in hippocampal slice cultures. Therefore, we trained a support vector machine (SVM) to classify images into either “high” or “low” neuronal damage and used naïve Bayes, discriminant analysis, random forest, and a multilayer perceptron (MLP) as classifiers for segmentation of dead cells. In our final models, pixel-wise accuracies of up to 0.97 were achieved using the MLP classifier. Furthermore, a SVM-based post-processing step was introduced to differentiate between false-positive and false-negative detections using morphological features. As only very few false-positive objects and thus training data remained when using the final model, this approach only mildly improved the results. A final object splitting step using Hough transformations was used to account for overlap, leading to a recall of up to 97.6% of the manually assigned PI-positive dead cells. Taken together, we present an analysis tool that can help to objectively and reproducibly analyze neuronal damage in brain-derived slice cultures, taking advantage of the morphology of pycnotic cells for segmentation, object splitting, and identification of false positives. Frontiers Media S.A. 2021-10-08 /pmc/articles/PMC8531652/ /pubmed/34690679 http://dx.doi.org/10.3389/fnins.2021.740178 Text en Copyright © 2021 Hohmann, Dehghani and Hohmann. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Hohmann, Urszula
Dehghani, Faramarz
Hohmann, Tim
Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title_full Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title_fullStr Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title_full_unstemmed Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title_short Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features
title_sort assessment of neuronal damage in brain slice cultures using machine learning based on spatial features
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531652/
https://www.ncbi.nlm.nih.gov/pubmed/34690679
http://dx.doi.org/10.3389/fnins.2021.740178
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