Toward self-contained cardiac magnetic resonance elastography: Deep learning-based segmentation of the left ventricular myocardium.
Authors
Affiliations (8)
Affiliations (8)
- Department of Physics and Medical Engineering, Faculty of Mathematics and Applied Physics, Rzeszów University of Technology, Rzeszów, Poland; Department of Radiology, Independent Public Health Care Center of the Ministry of Interior and Administration in Rzeszów, Rzeszów, Poland; Faculty of Health Sciences and Psychology, Medical College, University of Rzeszów, Rzeszów, Poland. Electronic address: [email protected].
- Institute of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
- Faculty of Health Sciences and Psychology, Medical College, University of Rzeszów, Rzeszów, Poland.
- Institute of Physics, Faculty of Exact and Technical Sciences, University of Rzeszów, Rzeszów, Poland; Faculty of Medicine, Medical College, University of Rzeszów, Rzeszów, Poland.
- Department of Radiology, Independent Public Health Care Center of the Ministry of Interior and Administration in Rzeszów, Rzeszów, Poland.
- Faculty of Medicine, Medical College, University of Rzeszów, Rzeszów, Poland.
- Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland.
- Institute of Physics, Faculty of Exact and Technical Sciences, University of Rzeszów, Rzeszów, Poland.
Abstract
Cardiac magnetic resonance elastography (MRE) is an emerging modality for noninvasive assessment of left ventricular (LV) myocardial stiffness. Accurate LV myocardium delineation is essential for MRE analysis, yet current workflows often rely on manual annotation and additional structural MRI. It remains uncertain whether native cardiac MRE data alone are sufficient for reliable automated LV segmentation. To evaluate deep learning approaches for LV myocardium segmentation on cardiac MRE data and to assess the influence of input representation and automation strategy on segmentation performance. Cardiac MRE data from 16 healthy male volunteers were used to train and evaluate two contemporary segmentation frameworks, nnU-Net v2 and MedSAM. Reader 1 annotated the full dataset using MRE magnitude images, and Reader 2 independently annotated the test set, enabling model performance to be benchmarked against inter-reader agreement. nnU-Net was trained using multiple input representations and training strategies. MedSAM was evaluated in zero-shot, semi-automated, fine-tuned, autoprompt, and box-regression configurations. Inter-reader Dice agreement was 0.79 ± 0.03. The best nnU-Net model, trained on fully averaged normalized magnitude images, achieved a Dice score of 0.82 ± 0.04. Performance was lower with magnitude-plus-phase and real-plus-imaginary inputs, with Dice scores of 0.65 ± 0.21 and 0.60 ± 0.20, respectively, and also decreased with non-normalized magnitude input, which yielded a Dice score of 0.75 ± 0.05. The best MedSAM result was obtained with a semi-automated fine-tuned variant using strong ROI smoothing, which achieved a Dice score of 0.82 ± 0.02. Fully automated MedSAM variants performed less well, with Dice scores of 0.68 ± 0.09 for autoprompt and 0.71 ± 0.08 for box regression. Cardiac MRE data alone demonstrated the feasibility of accurate LV myocardium segmentation, with nnU-Net and MedSAM both reaching inter-reader-level performance. These findings support direct segmentation of the LV myocardium from native cardiac MRE and represent a step toward a self-contained cardiac MRE workflow.