For augmentation that’s all bases covered. For more high-level or fully generative tasks I would also suggest mix-match (convex combo between similar samples). But you can’t justify that here bc you would have to relabel. Ultimately this does come down to too few images. If there’s a publicly available pretrained CT segmentation model you could fine-tune it to your task, or distill it’s weights to your model… just make sure they did a good job in the first place.

Also some other notes: I’d suggest sticking with distribution losses ie cross entropy. U-Net is sensitive to normalization so I’d also suggest training with and without normalized inputs.