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 image-based 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）已被确立为识别解剖形态在人群间变异性的优秀工具。形状模型对给定队列中的所有样本采用一致的形状表示，有助于比较形状并识别可检测病理及辅助制定治疗方案的变异。在医学成像中，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、配准和纹理降噪。深度学习模型已展现出直接从体素图像中学习形状表示的卓越能力，催生了高效且有效的图像到统计形状模型（Image-to-SSM）方法。然而，这类模型需要海量数据，而医学数据的有限可用性导致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通过生成形状增强样本，已成功帮助Image-to-SSM网络达到与传统SSM方法相当的精度。但此类增强方法仅聚焦于形状增强，而深度学习模型存在的基于图像的纹理偏差会导致次优模型。本文提出一种面向Image-to-SSM框架的实时数据增强新策略，通过利用数据相关噪声生成或纹理增强实现。所提框架作为Image-to-SSM网络的对抗性训练模块，可增强多样且具有挑战性的含噪样本。我们的方法通过引导模型关注底层几何结构而非仅依赖像素值，实现了精度提升。