NeuroNet is a deep convolutional neural network mimicking multiple popular
and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
The network is trained on 5,000 T1-weighted brain MRI scans from the UK Biobank
Imaging Study that have been automatically segmented into brain tissue and
cortical and sub-cortical structures using the standard neuroimaging pipelines.
Training a single model from these complementary and partially overlapping
label maps yields a new powerful "all-in-one", multi-output segmentation tool.
The processing time for a single subject is reduced by an order of magnitude
compared to running each individual software package. We demonstrate very good
reproducibility of the original outputs while increasing robustness to
variations in the input data. We believe NeuroNet could be an important tool in
large-scale population imaging studies and serve as a new standard in
neuroscience by reducing the risk of introducing bias when choosing a specific
software package.

NeuroNet 是一个基于深度卷积神经网络的脑分割工具，可以训练单个模型以提高各种脑成分的分割能力，并减少处理时间而增加结果的稳定性。