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Related Experiment Video

Updated: Jun 19, 2026

A Microscopic 2,3,5-Triphenyltetrazolium Chloride Assay for Accurate and Reliable Analysis of Myocardial Injury
11:17

A Microscopic 2,3,5-Triphenyltetrazolium Chloride Assay for Accurate and Reliable Analysis of Myocardial Injury

Published on: November 28, 2025

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

Chenchu Xu, Run Wang, Ronghui Qi

    IEEE Transactions on Medical Imaging
    |June 17, 2026
    PubMed
    Summary
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    This study introduces MTMS, a novel contrast-free myocardial infarction segmentation model that bypasses the need for paired imaging data. MTMS effectively integrates cardiac biomechanics for improved segmentation accuracy without contrast agents.

    Area of Science:

    • Medical Imaging
    • Cardiovascular Disease Analysis
    • Artificial Intelligence in Medicine

    Background:

    • Contrast-free myocardial infarction (MI) segmentation is crucial for patient safety, avoiding risks associated with contrast agents (CAs).
    • Existing methods struggle with inter-modality slice misalignments and require paired CINE and contrast-enhanced images, which are difficult to acquire.

    Purpose of the Study:

    • To develop the first label-free training and contrast-free MI segmentation model (MTMS) that does not require paired datasets.
    • To incorporate cardiac biomechanical knowledge into contrast-free MI segmentation using a self-supervised paradigm.

    Main Methods:

    • MTMS utilizes dual upstream guidance: a Spatiotemporal Structural Evolution Module for deformation trajectories and a Cardiac Mechanics-Driven Analysis Module for stress responses.

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  • Self-supervised learning is achieved through iterative pseudo-label refinement, combining structural and biomechanical cues via a Dual-Domain Interaction Module.

    • Main Results:

    • MTMS achieved a Dice score of 0.698 and HD95 of 19.634 on 370 clinical cases.
    • The model outperformed seven state-of-the-art methods, showing significant improvements in Dice and HD95 metrics.

    Conclusions:

    • MTMS demonstrates the potential of integrating biomechanical knowledge into contrast-free MI segmentation through self-supervised learning.
    • This approach advances the development of effective and safer MI segmentation techniques, reducing reliance on contrast agents.