Automatic Dendritic Spine Detection And Analysis Research In Microscope Images

The branches extending from the cell body of neurons, the dendrites, receive more than 90% of the synaptic contacts made into that neuron. In many neurons of the mammalian brain, excitatory synapses involve specialized structures called dendritic spines that protrude from the dendrites and contain the molecules and organelles involved in the postsynaptic processing of the synaptic information. Neuron morphology, as captured in part by the structure of these spines, is illustrative of neuronal function and can be instrumental in better understanding the dysfunction seen in neurodegenerative conditions such as Alzheimer’s and Parkinson’s disease. Hence researchers have shown great interest in quantitatively studying dendritic spine morphology and density both statically and as a function of time. Such studies are typically carried out through the analysis of data collected from a range of microscopy modalities including confocal laser scanning microscopy (CLSM) and two-photon laser scanning microscopy (2PLSM).A strong correlation exists between the functional properties of a neuron and its morphologic structure. Automatic dendritic spine detection from high resolution microscopic images is an important step for such morphological studies. However separation and measurement of dendritic spines are difficult due to the complexity of the images. Most of the current morphologic analyses involve a significant component of manual labor. An algorithm based on endpoint match-searching and segmentation-sampling curve fitting is presented to detect the edge of the dendrite automatically, separate dendritic spines and calculate their size, number and density in complex images. Analysis of the results shows that it provides an efficient, accurate tool for dendritic spine analysis.