Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning

Abstract

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patients suffering from neurodegenerative diseases, increasingly involves image-based high-content screening and analysis. The first critical step toward fully automated high-content image analyses in such studies is to detect all neuronal cells and distinguish them from possible non-neuronal cells or artifacts in the images. Here we investigate the performance of well-established machine learning techniques for this purpose. These include support vector machines, random forests, k-nearest neighbors, and generalized linear model classifiers, operating on an extensive set of image features extracted using the compound hierarchy of algorithms representing morphology, and the scale-invariant feature transform. We present experiments on a dataset of rat hippocampal neurons from our own studies to find the most suitable classifier(s) and subset(s) of features in the common practical setting where there is very limited annotated data for training. The results indicate that a random forests classifier using the right feature subset ranks best for the considered task, although its performance is not statistically significantly better than some support vector machine based classification models.

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Acknowledgments

This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness (project numbers MTM2014-54151-P, UNLC08-1E-002, UNLC13-13-3503), the University of La Rioja (project number FPI-UR-13), the European Regional Development Fund (FEDER) of the European Union, the Netherlands Organization for Scientific Reseach (project number 612.001.018), and the Erasmus University Medical Center Fellowship Program. Carlos Fernandez-Lozano was supported by a Juan de la Cierva postdoctoral fellowship grant (Spanish Ministry of Economy, Industry and Competitiveness, FJCI-2015-26071).

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Correspondence to Gadea Mata.

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Gadea Mata, Miroslav Radojević and Carlos Fernandez-Lozano contributed equally to this work.

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Mata, G., Radojević, M., Fernandez-Lozano, C. et al. Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning. Neuroinform 17, 253–269 (2019). https://doi.org/10.1007/s12021-018-9399-4

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Keywords

  • Neuron detection
  • High-content analysis
  • Fluorescence microscopy
  • Machine learning