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Medical image segmentation on GPUs--a comprehensive review.

Erik Smistad1, Thomas L Falch2, Mohammadmehdi Bozorgi2

  • 1Norwegian University of Science and Technology, Sem Sælandsvei 7-9, 7491 Trondheim, Norway; SINTEF Medical Technology, Postboks 4760 Sluppen, 7465 Trondheim, Norway.

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Summary

Graphic processing units (GPUs) can accelerate medical image segmentation for diagnostics and planning. This review explores GPU acceleration for segmentation methods, highlighting benefits and limitations for efficient medical imaging.

Keywords:
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Area of Science:

  • Medical Imaging
  • Computer Vision
  • High-Performance Computing

Background:

  • Medical image segmentation is crucial for diagnostics, planning, and guidance.
  • Current segmentation methods are computationally intensive, hindering efficiency with growing data volumes.
  • Graphic Processing Units (GPUs) offer parallel processing capabilities for faster computation.

Purpose of the Study:

  • To investigate the application of GPUs for accelerating medical image segmentation.
  • To define criteria for efficient GPU utilization in segmentation.
  • To provide insights into GPU optimization for medical imaging.

Main Methods:

  • Review of existing medical image segmentation methods.
  • Evaluation of segmentation techniques based on GPU efficiency criteria.
  • Analysis of GPU implementation strategies and optimization techniques.

Main Results:

  • Most segmentation methods, due to their data-parallel nature, can benefit from GPU acceleration.
  • GPUs provide significant speedups compared to traditional Central Processing Units (CPUs).
  • Factors like synchronization and memory usage can impact achievable speedup.

Conclusions:

  • GPU acceleration is a promising approach to enhance the efficiency of medical image segmentation.
  • Careful consideration of GPU architecture and algorithm design is necessary for optimal performance.
  • Further research into GPU optimization can unlock greater potential for medical imaging applications.