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

Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function