  Fully-supervised airway segmentation has accomplished significant triumphs
over the years in aiding pre-operative diagnosis and intra-operative
navigation. However, full voxel-level annotation constitutes a labor-intensive
and time-consuming task, often plagued by issues such as missing branches,
branch annotation discontinuity, or erroneous edge delineation. label-efficient
solutions for airway extraction are rarely explored yet primarily demanding in
medical practice. To this end, we introduce a novel skeleton-level annotation
(SkA) tailored to the airway, which simplifies the annotation workflow while
enhancing annotation consistency and accuracy, preserving the complete
topology. Furthermore, we propose a skeleton-supervised learning framework to
achieve accurate airway segmentation. Firstly, a dual-stream buffer inference
is introduced to realize initial label propagation from SkA, avoiding the
collapse of direct learning from SkA. Then, we construct a geometry-aware
dual-path propagation framework (GDP) to further promote complementary
propagation learning, composed of hard geometry-aware propagation learning and
soft geometry-aware propagation guidance. Experiments reveal that our proposed
framework outperforms the competing methods with SKA, which amounts to only
1.96% airways, and achieves comparable performance with the baseline model that
is fully supervised with 100% airways, demonstrating its significant potential
in achieving label-efficient segmentation for other tubular structures, such as
vessels.
