Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images

TL;DR

This paper introduces a novel training approach that uses a pre-trained 2D Vision Transformer on natural images to improve 3D neuron segmentation, addressing data scarcity and enhancing performance.

Contribution

It proposes a 2D-to-3D weight transfer strategy leveraging natural image knowledge to boost neuron segmentation accuracy with limited data.

Findings

Achieved 8.71% performance improvement on BigNeuron benchmark.

Utilized large-scale natural image pre-training to enhance neuron segmentation.

Demonstrated data-efficient learning for 3D neuron reconstruction.

Abstract

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.