BPD-Neo: An MRI Dataset for Lung-Trachea Segmentation with Clinical Data for Neonatal Bronchopulmonary Dysplasia
This dataset addresses a problem for clinicians and researchers in neonatal care by providing a resource for developing segmentation tools to improve BPD diagnosis, though it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the lack of a specialized dataset for neonatal bronchopulmonary dysplasia (BPD) by creating BPD-Neo, an MRI dataset with lung-trachea segmentations for 40 neonates, primarily diagnosed with BPD, to support algorithm development for non-invasive diagnosis.
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.