Deep learning models usually require sufficient training data to achieve high
accuracy, but obtaining labeled data can be time-consuming and labor-intensive.
Here we introduce a template-based training method to train a 3D U-Net model
from scratch using only one population-averaged brain MRI template and its
associated segmentation label. The process incorporated visual perception
augmentation to enhance the model's robustness in handling diverse image inputs
and mitigating overfitting. Leveraging this approach, we trained 3D U-Net
models for mouse, rat, marmoset, rhesus, and human brain MRI to achieve
segmentation tasks such as skull-stripping, brain segmentation, and tissue
probability mapping. This tool effectively addresses the limited availability
of training data and holds significant potential for expanding deep learning
applications in image analysis, providing researchers with a unified solution
to train deep neural networks with only one image sample.

通过模板训练方法，使用一种人口平均脑 MRI 模板和相关分割标签，从头开始训练 3D U-Net 模型，在视觉感知增强和减轻过拟合的基础上，实现对鼠、大鼠、印度狨猴、恒河猴和人类脑 MRI 进行头骨去除、脑分割和组织概率映射等分割任务的训练，有效解决数据有限的问题，扩展深度学习在图像分析中的应用，为研究人员提供了仅需一张图像样本即可训练深度神经网络的统一解决方案。