Crossover Structure Separation With Application to Neuron Tracing in Volumetric Images

Abstract

Morphology reconstruction of neurons from 3-D microscopic images is essential to neuroscience research. However, many reconstructions may contain errors and ambiguities because of the crossover neuronal fibers. In this article, an automatic algorithm is proposed for the detection and separation of crossover structures and is applied to neuron tracing for improving the neuron reconstruction results. First, a spherical-patches extraction (SPE)-Net is employed to detect the 3-D neuron crossover points and locate the crossover structures in neuron volumetric images. Second, a multiscale upgraded ray-shooting model (MSURS) is proposed to obtain robust results at different scales with high confidence and is employed to extract the crossover neuronal structure features. Then, a crossover structure separation (CSS) method is developed to eliminate the false connections of crossover structures and generate deformed separated neuronal fibers based on the extracted features to replace the original neurites signals. Experiments demonstrate that the SPE-Net for crossover point detection achieves average precision and recall rates of 73.89% and 79.66%, respectively, and demonstrate the proposed CSS method can improve 20.46% the performance of the reconstructions on average. The results confirm that the proposed method can effectively improve the neuron tracing results in volumetric images.