Statistical shape models (SSM) have been well-established as an excellent
tool for identifying variations in the morphology of anatomy across the
underlying population. Shape models use consistent shape representation across
all the samples in a given cohort, which helps to compare shapes and identify
the variations that can detect pathologies and help in formulating treatment
plans. In medical imaging, computing these shape representations from CT/MRI
scans requires time-intensive preprocessing operations, including but not
limited to anatomy segmentation annotations, registration, and texture
denoising. Deep learning models have demonstrated exceptional capabilities in
learning shape representations directly from volumetric images, giving rise to
highly effective and efficient Image-to-SSM. Nevertheless, these models are
data-hungry and due to the limited availability of medical data, deep learning
models tend to overfit. Offline data augmentation techniques, that use kernel
density estimation based (KDE) methods for generating shape-augmented samples,
have successfully aided Image-to-SSM networks in achieving comparable accuracy
to traditional SSM methods. However, these augmentation methods focus on shape
augmentation, whereas deep learning models exhibit texture bias results in
sub-optimal models. This paper introduces a novel strategy for on-the-fly data
augmentation for the Image-to-SSM framework by leveraging data-dependent noise
generation or texture augmentation. The proposed framework is trained as an
adversary to the Image-to-SSM network, augmenting diverse and challenging noisy
samples. Our approach achieves improved accuracy by encouraging the model to
focus on the underlying geometry rather than relying solely on pixel values.

基于深度学习模型的图像到统计形状模型 (Image-to-SSM) 的在线数据增强框架通过引入数据相关噪声生成或纹理增强，提供了改进的准确性，使模型能够关注底层几何形状而不仅仅依赖像素值。