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Deep Learning and Texture-Based Semantic Label Fusion for Brain Tumor Segmentation.

L Vidyaratne1, M Alam1, Z Shboul1

  • 1Vision Lab in Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529.

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|March 20, 2018
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Summary
This summary is machine-generated.

This study introduces a novel semantic label fusion algorithm for robust brain tumor segmentation from MRI scans. By combining texture-based and deep learning methods, it significantly improves accuracy, outperforming existing approaches.

Keywords:
Brain Tumor SegmentationConvolutional Neural NetworkDeep LearningLabel FusionRandom ForestTexture Features

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Brain tumor segmentation is crucial for treatment planning and therapy.
  • Existing segmentation methods, including hand-crafted and deep learning approaches, have limitations.
  • Combining diverse methods can potentially overcome individual weaknesses for more robust segmentation.

Purpose of the Study:

  • To develop and evaluate a semantic label fusion algorithm for improved brain tumor segmentation.
  • To combine the strengths of texture-based and deep learning segmentation methods.
  • To address limitations such as false positives and misclassifications in current techniques.

Main Methods:

  • Proposed a semantic label fusion algorithm integrating texture-based and deep learning segmentation.
  • Utilized the publicly available BRATS 2017 brain tumor segmentation challenge dataset for evaluation.
  • Investigated the impact of patient gender on segmentation performance.

Main Results:

  • The proposed fusion method demonstrated improved segmentation accuracy.
  • Successfully alleviated false positives common in texture-based methods.
  • Addressed false tumor tissue classification issues inherent in deep learning methods.
  • Achieved first place in the BRATS 2017 patient survival prediction task.

Conclusions:

  • Semantic label fusion offers a robust approach to brain tumor segmentation.
  • Combining complementary methods enhances segmentation performance by mitigating individual algorithm weaknesses.
  • The developed method shows promise for clinical applications and advanced tasks like survival prediction.