Automatic detection of neurons in high-content microscope images using machine learning approaches

  1. Mata, G. 1
  2. Radojevic, M. 2
  3. Smal, I. 2
  4. Morales, M. 3
  5. Meijering, E. 2
  6. Rubio, J. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Erasmus University Medical Center
    info

    Erasmus University Medical Center

    Róterdam, Holanda

    ROR https://ror.org/018906e22

  3. 3 Universitat Autònoma de Barcelona
    info

    Universitat Autònoma de Barcelona

    Barcelona, España

    ROR https://ror.org/052g8jq94

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Libro:
Proceedings - International Symposium on Biomedical Imaging

ISBN: 9781479923502

Año de publicación: 2016

Volumen: 2016-June

Páginas: 330-333

Congreso: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). Prague, Czech Republic 13-16 April 2016

Tipo: Aportación congreso

DOI: 10.1109/ISBI.2016.7493276 SCOPUS: 2-s2.0-84978378791 WoS: WOS:000386377400080 GOOGLE SCHOLAR

Resumen

The study of neuronal cell morphology and function in relation to neurological disease processes is of high importance for developing suitable drugs and therapies. To accelerate discovery, biological experiments for this purpose are increasingly scaled up using high-content screening, resulting in vast amounts of image data. For the analysis of these data fully automatic methods are needed. The first step in this process is the detection of neuron regions in the high-content images. In this paper we investigate the potential of two machine-learning based detection approaches based on different feature sets and classifiers and we compare their performance to an alternative method based on hysteresis thresholding. The experimental results indicate that with the right feature set and training procedure, machine-learning based methods may yield superior detection performance. © 2016 IEEE.