Key result
Deep learning matches inter-reader agreement for LV myocardium segmentation on cardiac MRE with 0.82 Dice scores.
Why the study?
Accurate LV myocardium delineation is essential for cardiac MRE analysis but currently relies on manual annotation and additional structural MRI, leaving it uncertain whether native cardiac MRE data alone suffice for reliable automated segmentation.
Deep learning models can accurately segment the left ventricular myocardium directly from native cardiac MRE data, achieving performance comparable to human readers.
Answers are grounded in this paper and cite their sources.
Supports feasibility of automated LV segmentation from native MRE alone; leaves open clinical validation before workflow adoption.
BACKGROUND: 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. PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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Atamaniuk et al. (2026) studied Healthy (n=16). Deep learning-based segmentation (nnU-Net v2 and MedSAM) vs. Manual annotation (inter-reader agreement) was evaluated on Dice score for LV myocardium segmentation. Deep learning models (nnU-Net and MedSAM) achieved accurate left ventricular myocardium segmentation from native cardiac MRE data with Dice scores of 0.82, matching inter-reader agreement (0.79).
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