Myocardial Temporal-Mechanical Self-Supervision Model for Contrast-Free Myocardial Infarction Segmentation With...

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 contrast-enhanced 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 contrast-free 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.