  Neuron reconstruction, one of the fundamental tasks in neuroscience, rebuilds
neuronal morphology from 3D light microscope imaging data. It plays a critical
role in analyzing the structure-function relationship of neurons in the nervous
system. However, due to the scarcity of neuron datasets and high-quality SWC
annotations, it is still challenging to develop robust segmentation methods for
single neuron reconstruction. To address this limitation, we aim to distill the
consensus knowledge from massive natural image data to aid the segmentation
model in learning the complex neuron structures. Specifically, in this work, we
propose a novel training paradigm that leverages a 2D Vision Transformer model
pre-trained on large-scale natural images to initialize our Transformer-based
3D neuron segmentation model with a tailored 2D-to-3D weight transferring
strategy. Our method builds a knowledge sharing connection between the abundant
natural and the scarce neuron image domains to improve the 3D neuron
segmentation ability in a data-efficiency manner. Evaluated on a popular
benchmark, BigNeuron, our method enhances neuron segmentation performance by
8.71% over the model trained from scratch with the same amount of training
samples.
