Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
This work addresses the challenge of robust tumor segmentation in CT scans for medical diagnosis, though it is incremental as it adapts augmentation techniques specifically for the CT modality.
The paper tackled the problem of poor generalization in deep learning-based CT image segmentation due to inappropriate intensity augmentations, and proposed a CT-specific augmentation method called Random windowing that improved model performance on challenging images with poor contrast or timing, outperforming state-of-the-art alternatives.
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.