ArXiv Preprint
Discontinuity in the delineation of peripheral bronchioles hinders the
potential clinical application of automated airway segmentation models.
Moreover, the deployment of such models is limited by the data heterogeneity
across different centres, and pathological abnormalities also make achieving
accurate robust segmentation in distal small airways difficult. Meanwhile, the
diagnosis and prognosis of lung diseases often rely on evaluating structural
changes in those anatomical regions. To address this gap, this paper presents a
patch-scale adversarial-based refinement network that takes in preliminary
segmentation along with original CT images and outputs a refined mask of the
airway structure. The method is validated on three different datasets
encompassing healthy cases, cases with cystic fibrosis and cases with COVID-19.
The results are quantitatively evaluated by seven metrics and achieved more
than a 15% rise in detected length ratio and detected branch ratio, showing
promising performance compared to previously proposed models. The visual
illustration also proves our refinement guided by a patch-scale discriminator
and centreline objective functions is effective in detecting discontinuities
and missing bronchioles. Furthermore, the generalizability of our refinement
pipeline is tested on three previous models and improves their segmentation
completeness significantly.
Zeyu Tang, Nan Yang, Simon Walsh, Guang Yang
2022-10-21
