, Volume 17, Issue 2, pp 253–269 | Cite as

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

  • Gadea MataEmail author
  • Miroslav Radojević
  • Carlos Fernandez-Lozano
  • Ihor Smal
  • Niels Werij
  • Miguel Morales
  • Erik Meijering
  • Julio Rubio
Original Article


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.


Neuron detection High-content analysis Fluorescence microscopy Machine learning 



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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Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of La RiojaLogroñoSpain
  2. 2.Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and RadiologyErasmus University Medical CenterRotterdamNetherlands
  3. 3.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  4. 4.Instituto de Investigación Biomédica de A CoruñaComplexo Hospitalario Universitario de A CoruñaA CoruñaSpain
  5. 5.Molecular Cognition Laboratory, Biophysics Institute, CSIC-UPV/EHUCampus Universidad del País VascoLeioaSpain

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