Automated 3D dendritic spine detection and analysis from two-photon microscopy

The functional significance of dendritic spines and their plasticity to a wide spectrum of developmental and pathological conditions has led to extensive studies based on spine morphology. The advances in image acquisition techniques and the associated generation of large 3d data sets of optical micrographs have not been accompanied by comparable advances in data analysis techniques. We present an automated 3d spine detection and quantification procedure suitable for images obtained by laser scanning microscopy. The image is first processed by deconvolution and the dendritic phase consisting of the neuronal cytoplasm is extracted by segmentation. Spines are detected as geometrical protrusions relative to the dendritic backbone. As very thin necks may not be imaged, some spine 'heads' may be detached from the dendrite and are detected as detached components. These detected heads are merged with spine 'bases' where appropriate. Morphological characterizations on spine length, volume, density and shape classifications are obtained. For time-lapse data, images are registered and individual spines are traced through the image sequence. Successful comparison results on spine lengths and densities with manual analysis are obtained. This method is highly automatic and allows detailed and objective quantification of the structure and dynamics of dendritic spines, which can be important predictors for the function of neural networks.