  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 networks. 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 image-based texture bias resulting 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.
