  Fully-automatic lung lobe segmentation is challenging due to anatomical
variations, pathologies, and incomplete fissures. We trained a 3D u-net for
pulmonary lobe segmentation on 49 mainly publically available datasets and
introduced a weighted Dice loss function to emphasize the lobar boundaries. To
validate the performance of the proposed method we compared the results to two
other methods. The new loss function improved the mean distance to 1.46 mm
(compared to 2.08 mm for simple loss function without weighting).
