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Accelerating Chan-Vese model with cross-modality guided contrast enhancement for liver segmentation.

Nitin Satpute1, Juan Gómez-Luna2, Joaquín Olivares1

  • 1Department of Electronic and Computer Engineering, Universidad de Córdoba, Spain.

Computers in Biology and Medicine
|August 4, 2020
PubMed
Summary

This study enhances liver segmentation in CT scans using a GPU-accelerated Chan-Vese algorithm and a novel contrast enhancement technique. The improved method significantly boosts speed and accuracy for clinical applications.

Keywords:
Chan–VeseContrast enhancementGPUImage segmentationPersistence

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Accurate liver segmentation is crucial for clinical diagnosis but is hindered by noise and low contrast in CT scans.
  • Existing segmentation algorithms, like Chan-Vese, are robust to noise but computationally slow, limiting real-time applications.
  • Low contrast in CT liver images further degrades segmentation quality.

Purpose of the Study:

  • To develop an efficient and accurate liver segmentation method for computed tomography (CT) scans.
  • To address the speed limitations of the Chan-Vese algorithm through GPU parallelization.
  • To improve segmentation quality by implementing a cross-modality guided contrast enhancement pre-processing step.

Main Methods:

  • Implemented a GPU-accelerated version of the Chan-Vese active contour model for liver segmentation.
  • Integrated a cross-modality guided contrast enhancement technique as a pre-processing step.
  • Evaluated segmentation performance using metrics such as Dice coefficient, sensitivity, and accuracy on CT abdominal scans.

Main Results:

  • The GPU implementation of Chan-Vese achieved an average speedup of 99.8 times (without enhancement) and 14.6 times (with enhancement) compared to CPU.
  • Liver segmentation accuracy improved significantly with contrast enhancement, with Dice scores rising from 0.656 to 0.877.
  • Enhanced images showed improved sensitivity (0.816 to 0.964) and accuracy (0.822 to 0.956).

Conclusions:

  • GPU acceleration and contrast enhancement dramatically improve the speed and accuracy of CT liver segmentation.
  • The proposed method offers a viable solution for real-time, high-quality liver segmentation in clinical settings.
  • This approach enhances the clinical utility of CT imaging for liver-related conditions.