BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a common complication among preterm neonates,
with portable X-ray imaging serving as the standard diagnostic modality in neonatal in-
tensive 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 corre-
sponding 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.