A 3D Neuronal Morphology Classification Approach Based on Convolutional Neural Networks

Abstract

Since biological neurons have complex and diverse spatial geometric structures, classifying neurons according to their geometries has been an important issue. At present, the main approaches of neuron classification generally extract the structural features of neurons by the specified methods, and then use such features for classification. However, there are mainly two problems for these approaches. One is that there is no feature extraction standard of neuronal morphology, and the other is that a lot of useful information for neuron classification may be lost. Therefore, using the convolutional neural network model, this paper proposes a new three-dimensional (3D) neuronal morphology classification approach without geometric feature extraction. This approach first converts SWC format data of neuron into the scaled 3D neuronal graphics, and obtains the voxel data of neuronal morphology, and then uses convolutional neural network for training and classification. The experimental results show that this approach can classify the brain region cholinergic neurons of drosophila melanogaster, and obtains better classification accuracy.