首个3D气道分叉检测数据集，助力肺部疾病研究
BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling
- 构建首个公开3D气道分叉标注数据集，含父支与子支边界框
- 在ATM22数据集上验证，不同最小边界框尺寸下有明确基线结果
- 提供完整预处理与模型训练流程，适合肺部影像算法开发者
胸腔计算机断层扫描（CT）可提供气道树复杂分支网络的详细信息，对理解呼吸系统疾病至关重要。气道分叉是空气分支处的关键解剖标志，对肺生理、疾病机制及病灶定位具有重要意义。尽管分叉分析价值显著，但缺乏专门标注的数据集严重制约了自动化检测或分割工具的发展。本文提出BifDet，首个面向3D气道分叉检测的公开数据集，填补了现有资源空白。该数据集基于ATM22开源队列，包含经仔细标注的CT扫描，涵盖父支与子支的分叉边界框。作为应用案例，我们对RetinaNet和DETR进行微调与评估，针对不同最小边界框尺寸提供详尽结果，为未来研究提供基准参考。
原文摘要 · Abstract (English)
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.
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