  Automatic 3D neuron reconstruction is critical for analysing the morphology
and functionality of neurons in brain circuit activities. However, the
performance of existing tracing algorithms is hinged by the low image quality.
Recently, a series of deep learning based segmentation methods have been
proposed to improve the quality of raw 3D optical image stacks by removing
noises and restoring neuronal structures from low-contrast background. Due to
the variety of neuron morphology and the lack of large neuron datasets, most of
current neuron segmentation models rely on introducing complex and
specially-designed submodules to a base architecture with the aim of encoding
better feature representations. Though successful, extra burden would be put on
computation during inference. Therefore, rather than modifying the base
network, we shift our focus to the dataset itself. The encoder-decoder backbone
used in most neuron segmentation models attends only intra-volume voxel points
to learn structural features of neurons but neglect the shared intrinsic
semantic features of voxels belonging to the same category among different
volumes, which is also important for expressive representation learning. Hence,
to better utilise the scarce dataset, we propose to explicitly exploit such
intrinsic features of voxels through a novel voxel-level cross-volume
representation learning paradigm on the basis of an encoder-decoder
segmentation model. Our method introduces no extra cost during inference.
Evaluated on 42 3D neuron images from BigNeuron project, our proposed method is
demonstrated to improve the learning ability of the original segmentation model
and further enhancing the reconstruction performance.
