Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.
Authors
Affiliations (7)
Affiliations (7)
- National Heart and Lung Institute Imperial College London London UK.
- Biological Imaging Centre Imperial College London London UK.
- School of Computer Science University of Birmingham Birmingham UK.
- MRC London Institute of Medical Sciences Imperial College London London UK.
- Department of Computing Imperial College London London UK.
- Department of Brain Sciences Imperial College London London UK.
- Data Science Institute Imperial College London London UK.
Abstract
Artificial intelligence-based cardiac motion mapping offers predictive insights into pulmonary hypertension (PH) disease progression and its impact on the heart. We proposed an automated deep learning pipeline for bi-ventricular segmentation and 3D wall motion analysis in PH rodent models for bridging the clinical developments. A data set of 163 short-axis cine cardiac magnetic resonance scans were collected longitudinally from monocrotaline (MCT) and Sugen-hypoxia (SuHx) PH rats and used for training a fully convolutional network for automated segmentation. The model produced an accurate annotation in < 1 s for each scan (Dice metric > 0.92). High-resolution atlas fitting was performed to produce 3D cardiac mesh models and calculate the regional wall motion between end-diastole and end-systole. Prominent right ventricular hypokinesia was observed in PH rats (-37.7% ± 12.2 MCT; -38.6% ± 6.9 SuHx) compared to healthy controls, attributed primarily to the loss in basal longitudinal and apical radial motion. This automated bi-ventricular rat-specific pipeline provided an efficient and novel translational tool for rodent studies in alignment with clinical cardiac imaging AI developments.