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Updated: Jan 10, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Sustainable Ultralightweight U-Net-Based Architecture for Myocardium Segmentation.

Jakub Filarecki1, Dorota Mockiewicz1, Agata Giełczyk1

  • 1Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland.

Journal of Clinical Medicine
|November 27, 2025
PubMed
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A new lightweight AI model offers efficient cardiac muscle segmentation comparable to U-Net but with significantly fewer parameters. This sustainable approach aids radiologists in diagnosis and treatment planning.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Medical image segmentation is crucial for diagnosis and treatment.
  • U-Net is the standard but computationally intensive.
  • Challenges exist in deploying large AI models in clinical settings.

Purpose of the Study:

  • To develop a lightweight AI architecture for myocardium segmentation.
  • To address the computational demands of current segmentation models.
  • To improve the efficiency and sustainability of AI in medical imaging.

Main Methods:

  • Utilized real-world MRI data from hospital patients.
  • Proposed a novel, lightweight neural network architecture.
  • Tailored the architecture specifically for cardiac muscle segmentation.
Keywords:
Green AIMRI imagesU-Netcomputer visionmyocardiumsegmentation

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Main Results:

  • Achieved segmentation performance comparable to state-of-the-art methods (IoU=0.7889, Dice=0.8780).
  • The proposed model uses only 263k parameters and 6.24 G FLOPs.
  • Demonstrated significant reduction in model complexity and computational cost.

Conclusions:

  • The lightweight architecture supports radiologists in enhancing diagnostic accuracy.
  • The approach is efficient, fast, and suitable for sustainable AI development.
  • Offers a promising, less complex alternative to existing state-of-the-art methods.