Brain MRI Segmentation using Template-Based Training and Visual
  Perception Augmentation
Fang-Cheng Yeh
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.