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Automatic prostate segmentation based on fusion between deep network and variational methods.

Lu Tan1, Antoni Liang1, Ling Li1

  • 1School of Electrical Engineering, Computing and Mathematical Sciences (Computing Discipline), Curtin University, Bentley, Western Australia, Australia.

Journal of X-Ray Science and Technology
|August 13, 2019
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Summary
This summary is machine-generated.

This study introduces a novel approach for segmenting prostate magnetic resonance images (MRI), improving accuracy by addressing intensity variations and boundary issues. The enhanced method significantly outperforms existing Convolutional Neural Network (CNN) models.

Keywords:
Automatic prostate segmentationconvolutional neural networksvariational models

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Prostate segmentation from MRI is crucial for guiding biopsies.
  • Convolutional Neural Networks (CNNs) are state-of-the-art but face challenges with MRI data quality.
  • Inconsistent intensity levels and vague boundaries in MRI hinder accurate prostate segmentation.

Purpose of the Study:

  • To segment the prostate gland from a diverse MRI dataset with varying image quality.
  • To enhance the accuracy and reliability of prostate MRI segmentation.

Main Methods:

  • A global histogram matching approach was used to standardize MRI intensity distributions.
  • Variational models were integrated to improve boundary detection and segmentation accuracy.
  • A fused approach combining histogram matching and variational models was developed.

Main Results:

  • The proposed method demonstrated significant improvements across seven evaluation metrics compared to the V-net model.
  • Key metrics showed increases in Dice Coefficient (11.2%) and Jaccard Index (13.7%).
  • Reductions in Mean Hausdorff Distance (16.1%) were observed, indicating better boundary adherence.

Conclusions:

  • The developed approach significantly enhances prostate MRI segmentation performance over existing CNN and variational models.
  • The method provides more accurate, smoother, and uniform segmentation results.
  • The improved segmentation better represents prostate shapes and adheres to real boundaries.