  We propose a novel procedure to improve liver and lesion segmentation from CT
scans for U-Net based models. Our method extends standard segmentation
pipelines to focus on higher target recall or reduction of noisy false-positive
predictions, boosting overall segmentation performance. To achieve this, we
include segmentation errors into a new learning process appended to the main
training setup, allowing the model to find features which explain away previous
errors. We evaluate this on semantically distinct architectures: cascaded two-
and three-dimensional as well as combined learning setups for multitask
segmentation. Liver and lesion segmentation data are provided by the Liver
Tumor Segmentation challenge (LiTS), with an increase in dice score of up to 2
points.
