Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
Direct application of natural-image augmentation methods to CT segmentation disregards the physical meaning of Hounsfield Unit (HU) values, leading to artifacts and poor generalizability. To address this, we propose Random Window-Width Augmentation (RWWA), a CT-specific intensity augmentation method that dynamically samples window width and level based on the empirical HU distribution—thereby preserving anatomical interpretability and physical consistency of HU values. RWWA significantly improves model robustness to low-contrast and multiphase CT images. As the first work to systematically expose the limitations of generic intensity augmentation in CT and introduce a modality-adapted enhancement strategy, RWWA achieves state-of-the-art performance across multiple multi-center liver tumor segmentation benchmarks. Notably, it yields average Dice score improvements of 2.3–4.1% on challenging cases, empirically validating the critical role of physics-aware augmentation in enhancing model generalization.