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Brain MR Image Enhancement for Tumor Segmentation Using 3D U-Net.

Faizad Ullah1,2, Shahab U Ansari2, Muhammad Hanif2

  • 1Artificial Intelligence in Healthcare, Intelligent Information Processing Lab, National Center of Artificial Intelligence, University of Engineering and Technology, Peshawar 25120, Pakistan.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study introduces a robust MRI pre-processing sequence for accurate brain tumor segmentation. Combining Gibbs ringing artifact removal and bias-field correction significantly improved 3D U-Net performance, outperforming existing methods.

Keywords:
Gibbs ringing artifactbrain tumor segmentationdeep learningimage enhancementmedical image processing

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neuro-oncology

Background:

  • Manual MRI analysis for tumor quantification is subjective and error-prone.
  • Automated tools are needed for accurate brain tumor segmentation.
  • Effective pre-processing is crucial for reliable automated analysis.

Purpose of the Study:

  • To investigate optimal pre-processing techniques for MRI data.
  • To enhance the accuracy of automatic brain tumor segmentation using 3D U-Net.
  • To establish a robust pre-processing pipeline for improved segmentation results.

Main Methods:

  • Investigated permutations of Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE).
  • Applied a 3D U-Net model for automatic brain tumor segmentation on pre-processed MRI data.
  • Benchmarked results against state-of-the-art methods from the BraTS 2018 challenge.

Main Results:

  • The combination of Gibbs ringing artifact removal and bias-field correction yielded the best segmentation performance.
  • Achieved mean Dice scores of 0.91 (whole tumor), 0.86 (tumor core), and 0.70 (enhancing tumor).
  • Testing Dice scores reached 0.90 (whole tumor), 0.83 (tumor core), and 0.71 (enhancing tumor), surpassing previous methods.

Conclusions:

  • A novel, robust pre-processing sequence significantly improves MRI segmentation accuracy.
  • The proposed method offers a reliable approach for automated brain tumor segmentation.
  • This technique provides a valuable advancement for neuro-oncological image analysis.