  Automatic pathological pulmonary lobe segmentation(PPLS) enables regional
analyses of lung disease, a clinically important capability. Due to often
incomplete lobe boundaries, PPLS is difficult even for experts, and most prior
art requires inference from contextual information. To address this, we propose
a novel PPLS method that couples deep learning with the random walker (RW)
algorithm. We first employ the recent progressive holistically-nested network
(P-HNN) model to identify potential lobar boundaries, then generate final
segmentations using a RW that is seeded and weighted by the P-HNN output. We
are the first to apply deep learning to PPLS. The advantages are independence
from prior airway/vessel segmentations, increased robustness in diseased lungs,
and methodological simplicity that does not sacrifice accuracy. Our method
posts a high mean Jaccard score of 0.888$\pm$0.164 on a held-out set of 154 CT
scans from lung-disease patients, while also significantly (p < 0.001)
outperforming a state-of-the-art method.
