  Pulmonary lobe segmentation is an important preprocessing task for the
analysis of lung diseases. Traditional methods relying on fissure detection or
other anatomical features, such as the distribution of pulmonary vessels and
airways, could provide reasonably accurate lobe segmentations. Deep learning
based methods can outperform these traditional approaches, but require large
datasets. Deep multi-task learning is expected to utilize labels of multiple
different structures. However, commonly such labels are distributed over
multiple datasets. In this paper, we proposed a multi-task semi-supervised
model that can leverage information of multiple structures from unannotated
datasets and datasets annotated with different structures. A focused
alternating training strategy is presented to balance the different tasks. We
evaluated the trained model on an external independent CT dataset. The results
show that our model significantly outperforms single-task alternatives,
improving the mean surface distance from 7.174 mm to 4.196 mm. We also
demonstrated that our approach is successful for different network
architectures as backbones.
