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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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GPU-based relative fuzzy connectedness image segmentation.

Ying Zhuge1, Krzysztof C Ciesielski, Jayaram K Udupa

  • 1Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. zhugey@mail.nih.gov

Medical Physics
|January 10, 2013
PubMed
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A new parallel algorithm, P-ORFC, significantly speeds up medical image segmentation for large datasets. This fuzzy connectedness algorithm achieves interactive speeds on GPUs, crucial for quantitative radiology and automatic anatomy recognition.

Area of Science:

  • Medical imaging
  • Computer vision
  • Computational science

Background:

  • Clinical radiology increasingly relies on quantitative analysis of large image datasets.
  • Efficient image segmentation is critical for practical quantitative radiology.
  • Existing fuzzy connectedness (FC) algorithms, like RFC and IRFC, are computationally intensive for large volumes.

Purpose of the Study:

  • To develop a parallel version of a fuzzy connectedness (FC) algorithm for rapid medical image segmentation.
  • To achieve interactive processing speeds for very large medical image data sets.
  • To enhance the practicality of quantitative radiology through faster segmentation.

Main Methods:

  • Implementation of a parallel algorithm, P-ORFC (parallel optimal relative fuzzy connectedness), using NVIDIA's Compute Unified Device Architecture (CUDA).

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  • Leveraging GPU acceleration to improve computational speed over traditional CPU-based iterative relative fuzzy connectedness (IRFC) algorithms.
  • Testing the algorithm on diverse data sets ranging from small to super data sizes.

    • Main Results:

    • Achieved significant speedup factors (17.5× to 32.8×) on an NVIDIA Tesla C1060 GPU.
    • The P-ORFC algorithm's output closely approximates IRFC results, falling between RFC and IRFC objects.
    • Demonstrated interactive segmentation speeds for even the largest medical image data sets.

    Conclusions:

    • A parallel FC algorithm (P-ORFC) has been successfully developed and implemented on NVIDIA GPUs.
    • Interactive segmentation speeds are now achievable for large-scale medical imaging data.
    • GPU-accelerated segmentation holds significant potential for advancing automatic anatomy recognition in clinical radiology.