Dendritic spines are dynamic, submicron-scale protrusions on neuronal dendrites that receive neuronal inputs. Morphological changes in the dendritic spine often reflect alterations in physiological conditions and are indicators of various neuropsychiatric conditions. However, owing to the highly dynamic and heterogeneous nature of spines, accurate measurement and objective analysis of spine morphology are major challenges in neuroscience research. Most conventional approaches for analyzing dendritic spines are based on two-dimensional (2D) images, which barely reflect the actual three-dimensional (3D) shapes. Although some recent studies have attempted to analyze spines with various 3D-based features, it is still difficult to objectively categorize and analyze spines based on 3D morphology. Here, we propose a unified visualization framework for an interactive 3D dendritic spine analysis system, DXplorer, that displays 3D rendering of spines and plots the high-dimensional features extracted from the 3D mesh of spines. With this system, users can perform the clustering of spines interactively and explore and analyze dendritic spines based on high-dimensional features. We propose a series of high-dimensional morphological features extracted from a 3D mesh of dendritic spines. In addition, an interactive machine learning classifier with visual exploration and user feedback using an interactive 3D mesh grid view ensures a more precise classification based on the spine phenotype. A user study and two case studies were conducted to quantitatively verify the performance and usability of the DXplorer. We demonstrate that the system performs the entire analytic process effectively and provides high-quality, accurate, and objective analysis.

This letter proposes an accurate human pose estimation method that uses a modified kernel density approximation (m-KDA) to multiple pose hypotheses. Existing methods show poor human pose estimation because of cluttered background or self-occlusion by the human. To improve the pose estimation accuracy, we propose to use m-KDA to aggregate multiple pose estimation results. First, we use the flexible mixture-of-parts model (FMM) to estimate the human poses then use the top-M scores to choose the good pose hypotheses. Second, we aggregate the top-M pose hypotheses with the m-KDA, in which each kernel density function is modified by each pose's score value and each pose's compatibility function that represents how far each pose hypothesis is departed from the nominal value of top-M pose hypotheses. Third, we determine the optimal pose configuration by repeating the above m-KDA computation, starting from the root part (head) to the leaf parts (hands and feet), sequentially. In pose estimation experiments on two benchmark datasets (PARSE and LSP), the proposed method achieved 1.5-4.0% improvement in the percentage of correct localized parts (PCP) over the state-of-the-art methods.