Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation

2025-10-09

AI Summary

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.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Manipulation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable automated medical image analysis for detecting and delineating tumors in CT images, thereby reducing clinicians' workload. Achieving generalization capabilities in limited data domains, such as radiology, requires modern DL models to be trained with image augmentation. However, naively applying augmentation methods developed for natural images to CT scans often disregards the nature of the CT modality, where the intensities measure Hounsfield Units (HU) and have important physical meaning. This paper challenges the use of such intensity augmentations for CT imaging and shows that they may lead to artifacts and poor generalization. To mitigate this, we propose a CT-specific augmentation technique, called Random windowing, that exploits the available HU distribution of intensities in CT images. Random windowing encourages robustness to contrast-enhancement and significantly increases model performance on challenging images with poor contrast or timing. We perform ablations and analysis of our method on multiple datasets, and compare to, and outperform, state-of-the-art alternatives, while focusing on the challenge of liver tumor segmentation.

Problem

• Addressing poor generalization from naive intensity augmentations in CT scans
• Proposing CT-specific augmentation using Hounsfield Unit distributions
• Improving liver tumor segmentation robustness to contrast variations

Innovation

• Random windowing augmentation for CT images
• Utilizes Hounsfield Unit distribution for robustness
• Improves liver tumor segmentation performance