  One of the critical steps in improving accurate single neuron reconstruction
from three-dimensional (3D) optical microscope images is the neuronal structure
segmentation. However, they are always hard to segment due to the lack in
quality. Despite a series of attempts to apply convolutional neural networks
(CNNs) on this task, noise and disconnected gaps are still challenging to
alleviate with the neglect of the non-local features of graph-like tubular
neural structures. Hence, we present an end-to-end segmentation network by
jointly considering the local appearance and the global geometry traits through
graph reasoning and a skeleton-based auxiliary loss. The evaluation results on
the Janelia dataset from the BigNeuron project demonstrate that our proposed
method exceeds the counterpart algorithms in performance.
