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.

翻译：深度学习模型通常需要充足的训练数据才能达到较高精度，但获取标注数据往往耗时费力。本文提出一种基于模板的训练方法，仅使用一个群体平均脑MRI模板及其对应分割标签，即可从零开始训练3D U-Net模型。该过程融合了视觉感知增强技术，以提升模型处理多样化图像输入的鲁棒性并缓解过拟合问题。通过该方法，我们训练了针对小鼠、大鼠、狨猴、猕猴及人类脑部MRI的3D U-Net模型，实现了颅骨剥离、脑区分割及组织概率图谱绘制等分割任务。该工具有效解决了训练数据不足的难题，在拓展深度学习在图像分析领域应用方面具有重要潜力，为研究者提供了仅凭单张图像样本即可训练深度神经网络的统一解决方案。