Cross-over Structure Separation with Application to Neuron Tracing in Volumetric Images

Morphology reconstruction of neurons from 3D microscopic images is essential to neuroscience research. However, many reconstructions may contain errors and ambiguities because of the cross-over neuronal fibers. In this paper, an automatic algorithm is proposed for the detection and separation of cross-over structures and is applied to neuron tracing for improving the neuron reconstruction results. First, an SPE-Net is employed to detect the 3D neuron cross-over points and locate the cross-over 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 cross-over neuronal structure features. Then, a cross-over structure separation method (CSS) is developed to eliminate the false connections of cross-over 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 cross-over 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.