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Related Experiment Video

Updated: Mar 23, 2026

3D Whole-heart Myocardial Tissue Analysis
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Interactive deep learning for myocardial scar segmentation using cardiovascular magnetic resonance.

Aida Moafi1, Danial Moafi2, Simran Shergill1

  • 1Department of Cardiovascular Sciences, University of Leicester, the National Institute for Health and Care Research Leicester Biomedical Research Centre and British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Leicester, UK.

Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance
|March 21, 2026
PubMed
Summary

This study introduces an AI tool for faster, more accurate myocardial scar quantification using late gadolinium-enhancement cardiovascular magnetic resonance (LGE-CMR). The system significantly improves repeatability and reduces segmentation time in clinical practice.

Keywords:
Clinical artificial intelligenceDeep learningFoundation modelHuman-in-the-loopMyocardial infarctionScar segmentation

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Area of Science:

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

Background:

  • Late gadolinium-enhancement cardiovascular magnetic resonance (LGE-CMR) is crucial for myocardial scar assessment after infarction.
  • Conventional scar segmentation methods are time-consuming, labor-intensive, and prone to variability.
  • There is a need for automated and reproducible scar quantification techniques.

Purpose of the Study:

  • To develop an interactive deep learning system for scar segmentation and quantification from LGE-CMR images.
  • To improve the accuracy, speed, and reproducibility of myocardial scar assessment.
  • To integrate a clinician-facing interface for real-time interaction and automated quantification.

Main Methods:

  • A deep learning framework utilizing a vision foundation model adapted for medical imaging was developed.
  • The system incorporated prompt-guided segmentation and a clinician-facing interface for interactive analysis.
  • Training involved a composite loss function to handle annotation uncertainty, and evaluation used expert manual annotations and the FWHM method.

Main Results:

  • The AI framework achieved expert-level segmentation performance (Dice=0.74±0.10) with low scar mass error (median 1.28g).
  • Repeatability analysis showed excellent inter- and intra-observer agreement (CCC=0.999), surpassing conventional methods.
  • Segmentation time was significantly reduced, averaging 65 ± 34 seconds per patient.

Conclusions:

  • The developed interactive deep learning framework enables fast, accurate, and highly reproducible myocardial scar quantification from LGE-CMR.
  • This human-in-the-loop system offers consistent performance for routine clinical workflows.
  • The tool has the potential to enhance risk stratification and therapeutic planning in patients with myocardial infarction.