Pulmonary hypoplasia, or underdeveloped lungs, is a final common pathway shared by many fetal diseases. Congenital diaphragmatic hernia, in which abdominal organs push into the chest, is the best-known example, but kidney malformations with low amniotic fluid, bladder outlet obstruction, bronchopulmonary malformations, and skeletal dysplasias can all leave a fetus with lungs too small to sustain life after birth. Fetal magnetic resonance imaging provides a way to quantify the problem: by tracing the lungs slice by slice, radiologists can calculate total lung volume and compare it with expected values for gestational age, producing numbers that guide decisions about delivery planning and even fetal surgery.

The bottleneck has always been the tracing itself. Manual segmentation of the lungs on fetal MRI typically takes more than thirty minutes, and longer when the anatomy is distorted, the fetus moves during the scan, or amniotic fluid levels are abnormal. Tertiary fetal imaging centers often perform only two to five fetal MRI examinations per day, partly because of this labor requirement. Existing automated models offered little help beyond the most common diagnosis, because most were trained exclusively on congenital diaphragmatic hernia cases or structurally normal fetuses, leaving the many other causes of pulmonary hypoplasia without a reliable tool.

To build a more broadly applicable system, the researchers assembled a retrospective dataset of fetal MRI examinations performed between January 2016 and March 2025. Of 1,203 examinations identified, 906 had complete, recoverable expert-adjudicated lung segmentations that had been created during routine clinical care and used for actual patient counseling. The cohort spanned gestational ages from 16 to 38 weeks, with a mean of 27.1 weeks, and included the full spectrum of pulmonary hypoplasia etiologies. The data were split into 724 training and 182 independent test examinations for total lung volume, with a smaller subset of 196 examinations, including thoracic cavity and mediastinal labels, used for a second model predicting percent predicted lung volume.

Technically, the team used nnU-Net v2, a self-configuring segmentation framework that automatically determines preprocessing steps such as image resampling, intensity normalization, and patch size. Models were trained on an NVIDIA A100 graphics processing unit using five-fold cross-validation with both two-dimensional and three-dimensional full-resolution configurations, and final predictions combined the two model types through an ensemble. Notably, the researchers deliberately worked with the clinically acquired coronal T2-weighted single-shot fast spin-echo image stacks rather than reconstructed three-dimensional volumes. These source stacks are available at the time of interpretation, require no extra reconstruction step, and are less dependent on slice-to-volume reconstruction, a design choice intended to support rapid, pragmatic deployment in real clinics.

The results were striking for total lung volume. The automated model achieved a mean Dice similarity coefficient of 0.81, indicating strong spatial overlap with expert contours, and an intraclass correlation coefficient of 0.955 with manual volumetry, a level conventionally classified as excellent. Bland-Altman analysis showed minimal systematic bias of about one milliliter, with 95 percent limits of agreement between roughly minus 15 and plus 13 milliliters. Median absolute percentage error was 11.1 percent, close to the ten percent benchmark the authors selected as approximating inter-reader variability in fetal lung volumetry. Average inference time was 3.8 seconds per examination, roughly a 450-fold speedup over manual tracing.

The second model, which estimates percent predicted lung volume by additionally segmenting the mediastinum and thoracic cavity, performed less well. Its Dice coefficient averaged 0.76, its intraclass correlation coefficient was 0.727, and median absolute percentage error rose to 14.5 percent. The authors attribute this to the compounded difficulty of segmenting multiple structures that are variably distorted by herniated abdominal contents, a thymus that resembles hypoplastic lungs on T2-weighted images, and mass effects that shift the chest anatomy. At the clinically meaningful level of risk-category assignment, automated observed-to-expected total lung volume matched manual assignments in 86.8 percent of test examinations, with most disagreements occurring between adjacent severity categories, whereas percent predicted lung volume agreed in only 54.1 percent of cases.

Performance was remarkably stable across the disease spectrum. The total lung volume model performed best on skeletal dysplasia, left-sided congenital diaphragmatic hernia, and renal disease, and remained reliable in the presence of severe motion artifact, oligohydramnios, and polyhydramnios. It struggled most with very large bronchopulmonary malformations whose lesion volume exceeded that of the lungs themselves, where the model sometimes mistook the lesion for lung tissue and overestimated lung volume. Accuracy also dipped in fetuses scanned before 20 weeks of gestation, though it improved with advancing gestational age, precisely when lung volume metrics matter most for predicting neonatal outcomes and planning interventions.

Crucially, the system is designed for human oversight rather than autonomous use. In a simulated human-in-the-loop workflow, an attending fetal radiologist reviewed and corrected the 25 lowest-performing outputs, those with a Dice coefficient below 0.70, in an average of just 3.1 minutes per case. Most corrections involved removing labels erroneously assigned to bronchopulmonary malformations or adjusting boundaries along the diaphragm and herniated contents. Combining four-second automated segmentation with a few minutes of expert adjudication still cuts the total workflow time roughly tenfold, potentially allowing fetal lung volumetry to be delivered on the same day as imaging, when counseling and multidisciplinary decisions are made.

The implications extend beyond the clinic. Scalable, reproducible lung segmentations are a prerequisite for radiomics-based research, in which small boundary inaccuracies can propagate into unreliable quantitative features across large heterogeneous datasets. The authors caution that their study was retrospective and single-institution, that risk-category thresholds were derived from left-sided congenital diaphragmatic hernia outcome data and may not generalize to other etiologies, and that external validation across institutions, scanners, and vendors remains necessary. Still, by collapsing a thirty-minute expert task into seconds while preserving expert review for the hardest cases, the work represents a concrete step toward making quantitative fetal lung assessment a routine, same-day part of prenatal care.

Subject of Research: Automated deep learning segmentation of fetal MRI lungs for rapid lung volume estimation in pulmonary hypoplasia

Article Title: Automated fetal magnetic resonance imaging lung segmentation for rapid lung volume estimation across diverse pulmonary hypoplasia phenotypes

Article References: Choi, J. J., Hegde, S., Dudley, J., Galganski, L. A., Kline-Fath, B. M., Somasundaram, E., & Dillman, J. R. (2026). Automated fetal magnetic resonance imaging lung segmentation for rapid lung volume estimation across diverse pulmonary hypoplasia phenotypes. Pediatric Radiology. https://doi.org/10.1007/s00247-026-06765-1

Image Credits: AI Generated

DOI: 10.1007/s00247-026-06765-1

Keywords: fetal MRI, lung segmentation, deep learning, pulmonary hypoplasia, congenital diaphragmatic hernia, nnU-Net, lung volume, prenatal diagnosis, artificial intelligence, medical imaging, pediatric radiology, human-in-the-loop