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Updated: Mar 15, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Contrast-Free Myocardial Infarction Segmentation with Attention U-Net.

Khaled Ali Deeb¹, Yasmeen Alshelle², Hala Hammoud²

¹Department of Information Processing and Management Systems, Bauman Moscow State Technical University, 105005 Moscow, Russia.

Diagnostics (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

This study introduces a deep learning framework for automatic cardiac segmentation and myocardial infarction detection using non-contrast cardiovascular magnetic resonance imaging, improving efficiency and accessibility.

Keywords:

CNN U-Net attention cardiac MRI cine CMR deep learning myocardial infarction segmentation

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Last Updated: Mar 15, 2026

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiology

Background:

Cardiovascular magnetic resonance (CMR) is the gold standard for cardiac assessment but manual segmentation is time-consuming and variable.
Deep learning (DL) automates segmentation, yet struggles with generalizability in non-contrast cine CMR for myocardial infarction (MI) detection.

Purpose of the Study:

To develop a DL framework for automated cardiac structure and MI segmentation using contrast-free cine CMR.
To enhance the clinical applicability of CMR by enabling contrast-independent cardiac assessment.

Main Methods:

Integrated multiple CNNs for cardiac structure segmentation and an attention-based DL model for MI localization.
Employed post-processing with stacked autoencoders and active contour modeling for anatomical consistency.
Evaluated performance using Dice Similarity Coefficient (DSC), Mean Contour Distance (MCD), and Hausdorff Distance (HD).

Main Results:

Achieved high Dice scores: 0.93 for LV cavity, 0.89 for LV myocardium, 0.91 for RV cavity.
Demonstrated reliable MI segmentation with a Dice score of 0.80 and high recall.
Showcased consistently low boundary errors across all segmented cardiac structures.

Conclusions:

The proposed DL framework enables accurate, contrast-free segmentation of cardiac structures and MI from cine CMR.
Facilitates broader clinical use, especially for patients with contrast contraindications or in resource-limited settings.
Supports scalable and reliable cardiac assessment independent of contrast agents.