Assessment of Neuronal Damage in Brain Slice Cultures Using Machine Learning Based on Spatial Features - Hohmann, Urszula; Dehghani, Faramarz; Hohmann, Tim (Frontiers in Neuroscience, 2021)
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 to classify images into either “high” or “low” neuronal damage and used naïve Bayes, discriminant analysis, random forest and a multilayer perceptron as classifiers for segmentation of dead cells. In our final models, pixel wise accuracies of up to 0.97 were achieved using the multilayer perceptron classifier. Furthermore, a support vector machine 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 the morphology of pycnotic cells for segmentation, object splitting and identification of false positives.