University of Houston

Dendritic spines form the postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by biologists. Existing neurobiology literatures show that the morphological plasticity of dendritic spines is highly correlated with their underlying cognitive functions (e.g., sensory experience, learning, memory). Manually segmenting and establishing the spine correspondence in time-lapse images are tedious and subject to human bias. Currently, most of the dendritic spine detection and tracking approaches are designed for 2D maximum intensity projection (MIP) image. The major drawbacks of the 2D MIP based methods are: (1) when 3D microscopy images are projected to a 2D plane, a significant amount of information such as spines that are orthogonal to the imaging plane is lost; and (2) dendritic structures which overlap along the projection direction are difficult to extract. Therefore, in this research, novel methods for automatically dendritic spine detection, segmentation, and tracking in 3D time-lapse microscopy neuron images are proposed to help biologists study the pathway of various neurological conditions.