构建新生儿肺气管分割数据集，助力支气管肺发育不良研究
BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia
- 基于40名早产儿的自由呼吸3D MRI扫描，实现肺与气管精准分割
- 包含临床诊断数据与验证过的分割模型，支持病因分析与算法开发
- 适合医学影像、儿科疾病研究者使用，推动无辐射影像技术应用
支气管肺发育不良（BPD）是早产儿常见并发症，目前新生儿重症监护室多依赖便携式X光诊断。但肺部磁共振成像（MRI）可避免镇静与辐射，提供更详细的病理机制信息。本研究构建了40名早产儿的MRI数据集，包含自由呼吸的3D stack-of-stars radial gradient echo（StarVIBE）序列扫描，以及对应的肺和气管语义分割标签。多数患儿被确诊为BPD。数据集还配套提供完整的临床信息及经临床评估验证的基准分割模型，旨在支持新生儿肺部影像分析算法的研发与临床研究。
原文摘要 · Abstract (English)
Bronchopulmonary dysplasia (BPD) is a common complication among preterm neonates, with portable X-ray imaging serving as the standard diagnostic modality in neonatal intensive care units (NICUs). However, lung magnetic resonance imaging (MRI) offers a non-invasive alternative that avoids sedation and radiation while providing detailed insights into the underlying mechanisms of BPD. Leveraging high-resolution 3D MRI data, advanced image processing and semantic segmentation algorithms can be developed to assist clinicians in identifying the etiology of BPD. In this dataset, we present MRI scans paired with corresponding semantic segmentations of the lungs and trachea for 40 neonates, the majority of whom are diagnosed with BPD. The imaging data consist of free-breathing 3D stack-of-stars radial gradient echo acquisitions, known as the StarVIBE series. Additionally, we provide comprehensive clinical data and baseline segmentation models, validated against clinical assessments, to support further research and development in neonatal lung imaging.
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