A novel approach for three dimensional dendrite spine segmentation and classification

Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying the biochemical and genetic pathways by examining the morphological changes of the dendritic spines at the intracellular level. Automatic dendritic spine detection from high resolution microscopic images is an important step for such morphological studies. In this paper, a novel approach to automated dendritic spine detection is proposed based on a nonlinear degeneration model. Dendritic spines are recognized as small objects with variable shapes attached to dendritic backbones. We explore the problem of dendritic spine detection from a different angle, i.e., the nonlinear degeneration equation (NDE) is utilized to enhance the morphological differences between the dendrite and spines. Using NDE, we simulated degeneration for dendritic spine detection. Based on the morphological features, the shrinking rate on dendrite pixels is different from that on spines, so that spines can be detected and segmented after degeneration simulation. Then, to separate spines into different types, Gaussian curvatures were employed, and the biomimetic pattern recognition theory was applied for spine classification. In the experiments, we compared quantitatively the spine detection accuracy with previous methods, and the results showed the accuracy and superiority of our methods.