The AIIB23 challenge was organized in conjunction with the MICCAI 2023 conference to address the challenges in airway modeling and mortality prediction for fibrotic lung disease. The dataset included 120 high-resolution CT (HRCT) scans with expert annotations of airway structures and mortality status, which were split into training, validation, and test sets.
Task I focused on airway segmentation, encouraging participants to develop robust and generalizable models for extracting airway trees, especially the small branches. The top-performing methods utilized techniques such as weighted general union loss, continuity loss, and targeted data sampling strategies to enhance the segmentation accuracy and efficiency. These models demonstrated improved performance on fibrotic lung disease cases compared to previous public datasets, highlighting the importance of specialized training on this complex pathology.
Task II focused on mortality prediction, where participants explored the use of radiomics features and end-to-end deep learning approaches to classify patients as alive or deceased at 63 weeks after the initial scan. The results showed that both feature-based and data-driven methods can provide valuable insights, with the top-performing team achieving an overall score of 0.7059, including high F1 score of 0.8493.
The AIIB23 challenge provided a comprehensive benchmark for the research community, showcasing the state-of-the-art in airway modeling and mortality prediction for fibrotic lung disease. The findings suggest that incorporating specific loss functions and data sampling strategies can significantly improve the capacity of airway segmentation models, particularly in handling the complex abnormalities observed in fibrotic lung disease. Additionally, the challenge highlighted the potential of imaging biomarkers, derived from both manual and automated approaches, for prognostic applications in this patient population.
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