Automated classification of dopaminergic neurons in the rodent brain

Authors:
Azadeh Alavi;Brenton Cavanagh;Gervase Tuxworth;Adrian Meedeniya;Alan Mackay-Sim;Michael Blumenstein
Affiliations:
School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia;National Centre for Adult Stem Cell Research, Griffith University, Nathan, QLD;School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia;National Centre for Adult Stem Cell Research, Griffith University, Nathan, QLD;National Centre for Adult Stem Cell Research, Griffith University, Nathan, QLD;School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Accurate morphological characterization of the multiple neuronal classes of the brain would facilitate the elucidation of brain function and the functional changes that underlie neurological disorders such as Parkinson's diseases or Schizophrenia. Manual morphological analysis is very time-consuming and suffers from a lack of accuracy because some cell characteristics are not readily quantified. This paper presents an investigation in automating the classification of dopaminergic neurons located in the brainstem of the rodent, a region critical to the regulation of motor behaviour and is implicated in multiple neurological disorders including Parkinson's disease. Using a Carl Zeiss Axioimager Z1 microscope with Apotome, salient information was obtained from images of dopaminergic neurons using a structural feature extraction technique. A data set of 100 images of neurons was generated and a set of 17 features was used to describe their morphology. In order to identify differences between neurons, 2-dimensional and 3-dimensional image representations were analyzed. This paper compares the performance of three popular classification methods in bioimage classification (Support Vector Machines (SVMs), Back Propagation Neural Networks (BPNNs) and Multinomial Logistic Regression (MLR)), and the results show a significant difference between machine classification (with 97% accuracy) and human expert based classification (72% accuracy).