  Pulmonary lobe segmentation is an important task for pulmonary disease
related Computer Aided Diagnosis systems (CADs). Classical methods for lobe
segmentation rely on successful detection of fissures and other anatomical
information such as the location of blood vessels and airways. With the success
of deep learning in recent years, Deep Convolutional Neural Network (DCNN) has
been widely applied to analyze medical images like Computed Tomography (CT) and
Magnetic Resonance Imaging (MRI), which, however, requires a large number of
ground truth annotations. In this work, we release our manually labeled 50 CT
scans which are randomly chosen from the LUNA16 dataset and explore the use of
deep learning on this task. We propose pre-processing CT image by cropping
region that is covered by the convex hull of the lungs in order to mitigate the
influence of noise from outside the lungs. Moreover, we design a hybrid loss
function with dice loss to tackle extreme class imbalance issue and focal loss
to force model to focus on voxels that are hard to be discriminated. To
validate the robustness and performance of our proposed framework trained with
a small number of training examples, we further tested our model on CT scans
from an independent dataset. Experimental results show the robustness of the
proposed approach, which consistently improves performance across different
datasets by a maximum of $5.87\%$ as compared to a baseline model.
