Abstract
A dendritic spine is a small membranous protrusion from a neuron's dendrite that typically receives input from a single synapse of an axon. Recent research shows that the morphological changes of dendritic spines have a close relationship with some specific diseases. The distribution of different dendritic spine phenotypes is a key indicator of such changes. Therefore, it is necessary to classify detected spines with different phenotypes online. Since the dendritic spines have complex three dimensional (3D) structures, current neuron morphological analysis approaches cannot classify the dendritic spines accurately with limited features. In this paper, we propose a novel semi-supervised learning approach in order to perform the online morphological classification of dendritic spines. Spines are detected by a new approach based on wavelet transform in the 3D space. A small training data set is chosen from the detected spines, which has the spines labeled by the neurobiologists. The remaining spines are then classified online by the semi-supervised learning (SSL) approach. Experimental results show that our method can quickly and accurately analyze neuron images with modest human intervention.
Highlights
In neuron-biology research, the morphological structures of neurons have been proven to have a close correlation with the neuron functional properties
To solve the dendritic spines morphological analysis problems efficiently, we propose a novel morphological analysis algorithm for dendritic spines based on a supervised learning (SSL) approach
We propose a novel morphological analysis algorithm for dendritic spines based on a SSL approach
Summary
In neuron-biology research, the morphological structures of neurons have been proven to have a close correlation with the neuron functional properties. Among all the morphological structures of neurons, the dendrite and the dendritic spines are the most functional parts that draw the concentration of neuron-biology researchers. The morphological analysis of dendritic spines is widely used in the neurobiology research such as the one on Alzheimer’s disease [1,2,3]. With the help of modern fluorescence microscopy methods, the detailed 3D shapes of dendrites and spines can be obtained by confocal laser scanning microscopy (CLSM) or two-photon laser scanning microscopy (2PLSM) [4]. Since dendritic spines have various shapes and their structures correlate highly with their underlying cognitive functions [5], it is crucial to detect and extract dendritic spines from the dendrite, and analyze the spines morphological structure efficiently
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