Abstract
Airway-related quantitative imaging biomarkers are crucial for examination,
diagnosis, and prognosis in pulmonary diseases. However, the manual delineation
of airway trees remains prohibitively time-consuming. While significant efforts
have been made towards enhancing airway modelling, current public-available
datasets concentrate on lung diseases with moderate morphological variations.
The intricate honeycombing patterns present in the lung tissues of fibrotic
lung disease patients exacerbate the challenges, often leading to various
prediction errors. To address this issue, the 'Airway-Informed Quantitative CT
Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was
organized in conjunction with the official 2023 International Conference on
Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway
structures were meticulously annotated by three experienced radiologists.
Competitors were encouraged to develop automatic airway segmentation models
with high robustness and generalization abilities, followed by exploring the
most correlated QIB of mortality prediction. A training set of 120
high-resolution computerised tomography (HRCT) scans were publicly released
with expert annotations and mortality status. The online validation set
incorporated 52 HRCT scans from patients with fibrotic lung disease and the
offline test set included 140 cases from fibrosis and COVID-19 patients. The
results have shown that the capacity of extracting airway trees from patients
with fibrotic lung disease could be enhanced by introducing voxel-wise weighted
general union loss and continuity loss. In addition to the competitive image
biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5,
p<0.0001) was revealed for survival prognostication compared with existing
clinical measurements, clinician assessment and AI-based biomarkers.