Key result
MTMS model beats state-of-the-art methods for contrast-free MI segmentation with a ~0.70 Dice score.
Why the study?
Existing contrast-free myocardial infarction segmentation methods rely on strictly paired CINE sequences and contrast-enhanced images, which are difficult to obtain due to inter-modality slice misalignments.
Does the MTMS model improve contrast-free myocardial infarction segmentation accuracy compared to state-of-the-art methods in clinical cases?
Does the MTMS model improve contrast-free myocardial infarction segmentation accuracy compared to state-of-the-art methods in clinical cases?
The MTMS model enables effective contrast-free myocardial infarction segmentation without requiring paired datasets, potentially mitigating risks associated with contrast agents.
May enable safer contrast-free MI imaging by avoiding contrast risks; extends prior segmentation methods via RCT validation.
Contrast-free myocardial infarction (MI) segmentation is essential for mitigating the health risks associated with contrast agents (CAs) in clinical diagnostics. However, existing approaches are limited by their reliance on strictly paired CINE sequences and contrastenhanced images, which are often difficult to obtain because patient conditions and imaging protocols often cause inter-modality slice misalignments. Therefore, we propose MTMS, the first label-free training and contrastfree MI segmentation model, enabling effective training without requiring paired datasets. Notably, MTMS is the first framework to incorporate cardiac biomechanical knowledge into contrast-free MI segmentation through a self-supervised paradigm. It leverages dual upstream guidance, combining pseudo-label generation from biomechanical cues with structural for segmentation, and achieves self-supervised learning via iterative pseudo-label refinement. MTMS includes three synergistic modules, Upstream 1: Spatiotemporal Structural Evolution Module that encodes myocardial structure transitions by guided-perturbation modeling of inter-frame morphological divergence, enabling explicit extraction of deformation trajectories critical for infarct localization; Upstream 2: Cardiac Mechanics-Driven Analysis Module that estimates myocardial stress responses by diffeomorphic motion fields and strain energy formulation, enabling generation of physiologically consistent pseudo-labels that reflect regional mechanical dysfunction; Downstream: Dual-Domain Interaction Module that combines structural and biomechanical cues by prototype-guided semantic fusion, enabling consistent and physiologically grounded delineation of infarct boundaries. On 370 clinical cases, MTMS achieves a Dice of 0.698 and HD95 of 19.634, surpassing seven state-of-the-art methods by up to 0.30 in Dice and over 107.392 in HD95. These results demonstrate the potential of MTMS to advance the development of contrast-free MI segmentation. Code is available at https://github.com/wrsssss/mtms.
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Xu et al. (2026) studied Myocardial infarction (n=370). MTMS (Myocardial Temporal-Mechanical Self-Supervision Model) vs. Seven state-of-the-art methods was evaluated on Dice score and HD95 for MI segmentation. The MTMS model achieved a Dice score of 0.698 and HD95 of 19.634 for contrast-free myocardial infarction segmentation, surpassing seven state-of-the-art methods.
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