Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
This work addresses the problem of improving diagnostic and prognostic tools for pulmonary fibrosis patients by providing benchmarks and methods for automated airway segmentation and biomarker discovery, though it is incremental as it builds on existing competition frameworks.
The paper tackled the challenge of automatically segmenting airway trees in pulmonary fibrosis patients, where manual delineation is time-consuming and existing datasets lack complex patterns, by organizing the AIIB23 competition; the result showed that introducing voxel-wise weighted general union loss and continuity loss enhanced segmentation, and a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication.
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.