BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling
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...
| Authors | Ali Keshavarzi, Quentin Bouniot, Benjamin M. Smith et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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