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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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A game-theoretic framework for landmark-based image segmentation.

Bulat Ibragimov1, Boštjan Likar, Franjo Pernus

  • 1Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia. bulat.ibragimov@fe.uni-lj.si

IEEE Transactions on Medical Imaging
|June 14, 2012
PubMed
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This study introduces a novel game-theoretic framework for accurate landmark-based image segmentation. The method precisely segments medical images, overcoming limitations of existing techniques.

Area of Science:

  • Medical image analysis
  • Computational imaging
  • Game theory applications

Background:

  • Landmark-based image segmentation is crucial for medical image analysis.
  • Existing methods often suffer from initialization sensitivity and local optima convergence.

Purpose of the Study:

  • To present a novel game-theoretic framework for robust landmark-based image segmentation.
  • To improve accuracy and precision in segmenting medical images.

Main Methods:

  • Formulating landmark detection as a game with players, strategies, and payoffs.
  • Utilizing an iterative scheme to find game equilibrium for landmark identification.
  • Applying dynamic programming for object boundary extraction.

Main Results:

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Published on: November 30, 2022

  • The game-theoretic framework achieves high accuracy and precision in segmentation.
  • Demonstrated effectiveness in segmenting lung fields and heart ventricles.
  • Outperforms existing techniques in terms of mean boundary distance and area overlap.

Conclusions:

  • The proposed framework offers a robust and accurate solution for landmark-based image segmentation.
  • It effectively addresses limitations of conventional methods, providing reliable results.