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.

翻译：统计形状模型（SSM）已成为识别目标人群解剖形态变异的重要工具。形状模型通过同一队列中所有样本的一致形状表示，有助于比较形状并识别可检测病理变化的变异，从而辅助制定治疗方案。在医学影像领域，从CT/MRI扫描中计算这些形状表示需要耗时预处理操作，包括但不限于解剖分割标注、配准和纹理去噪。深度学习模型已展现出直接从三维体图像中学习形状表示的卓越能力，催生了高效且效果显著的图像到SSM（Image-to-SSM）方法。然而，此类模型依赖大量数据，由于医学数据的有限性，深度学习模型容易过拟合。基于核密度估计（KDE）的离线数据增强技术虽已成功辅助Image-to-SSM网络达到与传统SSM方法相当的精度，但这些增强方法仅聚焦形状增强，而深度学习模型固有的纹理偏差会导致次优模型。本文提出一种面向Image-to-SSM框架的在线数据增强新策略，通过数据依赖性噪声生成或纹理增强实现增强。所提框架以对抗性方式训练Image-to-SSM网络，生成多样化且具挑战性的噪声样本。该方法通过引导模型关注底层几何结构而非单纯依赖像素值，显著提升了模型精度。