Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

摘要: A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) shapes with sufficient precision to distinguish morphologic types, throughput for robust statistical analysis. The necessity analyze large volumetric data sets accurately, efficiently, true 3D a major bottleneck deriving reliable relationships between altered neuronal function changes morphology. We introduce novel system automated detection, shape analysis classification of spines from laser scanning microscopy (LSM) images that directly addresses these limitations. is more accurate, at least an order magnitude faster, than existing technologies. By operating fully the algorithm resolves are undetectable standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering Rayburst Sampling generate profile diameter estimates used classify into while minimizing optical smear quantization artifacts. technique opens new horizons on objective evaluation synaptic plasticity, normal development aging, neurodegenerative disorders impair cognitive function.