  Deep learning has the potential to revolutionize medical practice by
automating and performing important tasks like detecting and delineating the
size and locations of cancers in medical images. However, most deep learning
models rely on augmentation techniques that treat medical images as natural
images. For contrast-enhanced Computed Tomography (CT) images in particular,
the signals producing the voxel intensities have physical meaning, which is
lost during preprocessing and augmentation when treating such images as natural
images. To address this, we propose a novel preprocessing and intensity
augmentation scheme inspired by how radiologists leverage multiple viewing
windows when evaluating CT images. Our proposed method, window shifting,
randomly places the viewing windows around the region of interest during
training. This approach improves liver lesion segmentation performance and
robustness on images with poorly timed contrast agent. Our method outperforms
classical intensity augmentations as well as the intensity augmentation
pipeline of the popular nn-UNet on multiple datasets.
