  The automatic lung lobe segmentation algorithm is of great significance for
the diagnosis and treatment of lung diseases, however, which has great
challenges due to the incompleteness of pulmonary fissures in lung CT images
and the large variability of pathological features. Therefore, we propose a new
automatic lung lobe segmentation framework, in which we urge the model to pay
attention to the area around the pulmonary fissure during the training process,
which is realized by a task-specific loss function. In addition, we introduce
an end-to-end pulmonary fissure generation method in the auxiliary pulmonary
fissure segmentation task, without any additional network branch. Finally, we
propose a registration-based loss function to alleviate the convergence
difficulty of the Dice loss supervised pulmonary fissure segmentation task. We
achieve 97.83% and 94.75% dice scores on our private dataset STLB and public
LUNA16 dataset respectively.
