mlsanityData to 14 Oct 2026

BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling

arXiv 2604.24999 ↗First posted 27 Apr 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 66 references that are in the map. First posted on arXiv 27 Apr 2026.

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References · 21

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9 more
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