NeuronIQ: A novel computational approach for automatic dendrite spines detection and analysis

Recent research has shown a strong correlation between the functional properties of a neuron and its morphologic structure. Current morphologic analyses typically involve a significant component of computer-assisted manual labor, which is very time-consuming and is susceptible to operator bias. We present a neuroinformatics system called neuron image quantitator (NeuronIQ), an integrated data processing pipeline for automatic dendrite spine detection, quantification, and analysis. The automation includes an adaptive thresholding method, a SNR based detached spine component detection method and an attached spine component detection method based on the estimation of local dendrite morphology. The morphology information obtained both manually and automatically is compared in detail. The spine detection results are also compared with other existing semi-automatic approaches. The comparison results show that our approach has 33% fewer false positives and 77% fewer false negatives on average.