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

Single Neuron Segmentation using Graph-based Global Reasoning with Auxiliary Skeleton Loss from 3D Optical Microscope Images