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The paper introduces BifDet, a novel, publicly available dataset of annotated 3D CT scans for airway bifurcation detection, addressing a significant gap in resources for respiratory disease research. The dataset contains bounding box annotations of airway bifurcations, encompassing both parent and daughter branches, derived from the ATM22 open-access cohort. As a benchmark, the authors fine-tuned and evaluated RetinaNet and DETR on the dataset, providing baseline performance metrics for future studies.
Finally, a dataset exists to train and benchmark algorithms for automatically detecting airway bifurcations in 3D CT scans, a crucial step towards understanding respiratory diseases.
Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the airway tree, which is essential for understanding various respiratory diseases. Airway bifurcations, where airway branches split, are crucial landmarks for understanding lung physiology, disease mechanisms and lesion localization. Despite the significance of bifurcation analysis, a notable lack of datasets annotated for this task hinders the development of advanced automated specialized detection or segmentation tools. In this paper, we introduce BifDet, the first publicly-available dataset specialized for 3D airway bifurcation detection, filling a critical gap in existing resources. Our dataset comprises carefully annotated CT scans from the ATM22 open-access cohort with bifurcation bounding boxes covering the parent and daughter branches. As a use-case for demonstrating the potential of BifDet, we fine-tune and evaluate RetinaNet and DETR for 3D airway bifurcations detection on CT scans. We provide detailed pipelines, including preprocessing steps and specific implementation design choices. Results are detailed over various categories of minimal bounding box sizes to serve as baseline to benchmark future research.
