Automatic Repair of 3-D Neuron Reconstruction Based on Topological Feature Points and an MOST-Based Repairer

The digital reconstruction of neurons is essential to various neuroscientific studies. Due to the existence of gaps and ambiguities in neuron images, the neuron tracing results generated by most automatic reconstruction algorithms may be incomplete, resulting in false negatives (FNs), which need to be repaired in proof editing. However, the automatic proof-editing methods for repairing FN branches have rarely been explored. In this study, we propose a proof-editing algorithm for automatically detecting and repairing the FN branches of the initial reconstruction, which is based on a multiscale upgraded ray (MUR)-shooting model and an MOST-based repairer. The MUR detects the FN branch and the corresponding branch direction vector by analyzing the multiscale intensity distribution features around a topological feature point. The topological feature points contain the junction points detected from the neuron image and the tip nodes extracted from the initial reconstruction. The MOST-based repairer is proposed to prevent the redundant reconstructions by assigning the detected branch direction vector as the initial tracing direction, which rejects the nodes returning to the traced area. The experimental results demonstrate clearly that the proposed method can reduce 20% of the false-negative rate at most. The experimental results confirm that the proposed method is extremely helpful for generating faithful reconstructions.

Automated summary

This study proposed a proof-editing algorithm for automatically detecting and repairing false negative branches in neuron reconstructions, utilizing a multiscale upgraded ray-shooting model and a MOST-based repairer. Experimental results demonstrated a reduction of up to 20% in the false-negative rate, confirming the effectiveness of the proposed method in generating faithful reconstructions.