  Motivation: 3D neuron segmentation is a key step for the neuron digital
reconstruction, which is essential for exploring brain circuits and
understanding brain functions. However, the fine line-shaped nerve fibers of
neuron could spread in a large region, which brings great computational cost to
the neuron segmentation. Meanwhile, the strong noises and disconnected nerve
fibers bring great challenges to the task. Results: In this paper, we propose a
3D wavelet and deep learning based 3D neuron segmentation method. The neuronal
image is first partitioned into neuronal cubes to simplify the segmentation
task. Then, we design 3D WaveUNet, the first 3D wavelet integrated
encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets
could assist the deep networks in suppressing data noises and connecting the
broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the
biggest available annotated neuronal image dataset, BigNeuron, to train 3D
WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to
generate the complete neuron, which is digitally reconstructed using an
available automatic tracing algorithm. The experimental results show that our
neuron segmentation method could completely extract the target neuron in noisy
neuronal images. The integrated 3D wavelets can efficiently improve the
performance of 3D neuron segmentation and reconstruction. Availability: The
data and codes for this work are available at
https://github.com/LiQiufu/3D-WaveUNet.
