Fully automated diaphragm segmentation on routine 2D-cine and 3D thoracic MRI: multireader validation.
Authors
Affiliations (8)
Affiliations (8)
- Department of Cardiothoracic Imaging, Pitié-Salpêtrière University Hospital AP-HP, Sorbonne University, Paris, France. [email protected].
- Paris-Saclay University, CEA, CNRS, Inserm, BioMaps, Orsay, France. [email protected].
- ICAN Foundation for Innovation in Cardiometabolism and Nutrition, Paris, France. [email protected].
- Paris-Saclay University, CEA, CNRS, Inserm, BioMaps, Orsay, France.
- ICAN Foundation for Innovation in Cardiometabolism and Nutrition, Paris, France.
- Department of Cardiothoracic Imaging, Pitié-Salpêtrière University Hospital AP-HP, Sorbonne University, Paris, France.
- Specialized and Emergency Imaging Department, Pitié-Salpêtrière University Hospital AP-HP, Sorbonne University, Paris, France.
- Sorbonne University, INSERM 1146, CNRS 7371, Laboratoire d'Imagerie Biomédicale, Paris, France.
Abstract
The diaphragm is central to respiratory mechanics and may be impaired in various diseases. Parametric magnetic resonance imaging (MRI) assessment is limited by time-consuming manual segmentation. We developed and validated fully automated diaphragm segmentation on two-dimensional (2D)-cine and three-dimensional (3D) thoracic MRI. In this single-center study, eligible adults without known diaphragmatic disorders underwent 2D-cine MRI, 3D MRI, or both. Two U-Net models (2D/3D) were trained using expert manual contours as ground truth. Performance was evaluated with normalized surface Dice (NSD) with 3-voxel tolerance and average surface distance (ASSD). In the main cohort, five blinded radiologists rated segmentation usability and image quality. An additional separate pathology cohort was assessed by two blinded readers using the same scale. Overall, 59 individuals contributed 70 MRI acquisitions: 29 women, 30 men; aged 49.3 ± 18.8 years (mean ± standard deviation). Inference required 5 s (2D) and 30 s (3D) versus 10 min and 4.5 h for manual annotation. Segmentation accuracy was high: NSD 0.95 ± 0.08 (2D) and 0.92 ± 0.06 (3D), with ASSD 1.97 ± 2.29 mm (2D) and 1.88 ± 1.97 mm (3D). In the main cohort, segmentations were rated clinically usable in 87.5% (2D) and 100% (3D). Mean image-quality scores were 4.0/5 (2D) and 4.2/5 (3D) and correlated with segmentation quality. In the pathology cohort (n = 5), segmentation usability for 2D images was 75% for reader 1 and 70% for reader 2 for 2D images and 80% for both readers for 3D volumes. Automated diaphragm segmentation on 2D-cine and 3D MRI is feasible, accurate, and time-efficient, supporting scalable, standardized diaphragm analysis as a basis for deriving diaphragm biomarkers. Question Can fully automated diaphragm segmentation make routine 2D-cine and 3D thoracic MRI analysis clinically feasible by replacing time-consuming manual delineation? Findings Automated segmentation achieved normalized surface Dice above 0.9, radiologist acceptability in 87.5-100% of cases, and inference times below 30 s Relevance statement Automated diaphragm segmentation on routine 2D-cine and 3D static MRI yields accurate, radiologist-acceptable segmentations with normalized surface Dice exceeding 0.9 and mean surface deviations below 2 mm. This approach may support standardized diaphragm assessment for objective quantification in thoracic and neuromuscular imaging pending larger multicenter validation.