BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
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.
