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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation.

Chaitra Dayananda1, Jae-Young Choi2, Bumshik Lee1

  • 1Department of Information and Communications Engineering, Chosun University, Gwangju 61452, Korea.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces an attention-based U-SegNet for brain MRI segmentation, improving accuracy and reducing parameters. The novel approach enhances feature extraction for precise segmentation of cerebrospinal fluid, gray matter, and white matter.

Keywords:
CNNbrain MRImulti global attentiontissue segmentation

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Area of Science:

  • Medical image analysis
  • Artificial intelligence in neuroimaging
  • Deep learning for segmentation

Background:

  • Convolutional Neural Networks (CNNs) are widely used for medical image segmentation but face challenges with redundant feature extraction and inaccurate long-range dependency modeling.
  • Conventional encoder-decoder architectures in CNNs can lead to inefficiencies and suboptimal segmentation accuracy for brain tissues.
  • Accurate segmentation of brain tissues in Magnetic Resonance Images (MRIs) is crucial for diagnosis and treatment planning.

Purpose of the Study:

  • To propose a novel attention-based U-SegNet architecture for enhanced multi-scale feature extraction in brain MRI segmentation.
  • To address the limitations of conventional CNNs, including redundant feature learning and poor long-range dependency modeling.
  • To improve the accuracy and computational efficiency of automatic brain tissue segmentation.

Main Methods:

  • Developed a multi-scale feature extraction method using attention-based convolutional learning within the U-SegNet architecture.
  • Integrated a global attention module to refine feature extraction and enhance the representational power of the CNN.
  • Incorporated an attention-based multi-scale fusion strategy to combine local features with global dependencies and utilized fire modules to reduce computational complexity.

Main Results:

  • Achieved high segmentation accuracies: 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM).
  • Demonstrated a 2.5% improvement in Dice Similarity Index (DSI) compared to previous U-SegNet approaches.
  • Reduced the number of learnable parameters by 4.5 times, indicating significant computational efficiency.

Conclusions:

  • The proposed attention-based U-SegNet effectively segments brain tissues in MRIs with improved accuracy and reduced computational cost.
  • The novel attention mechanisms enhance feature representation and integration of multi-scale information for precise segmentation.
  • This approach offers a reliable and efficient solution for automatic brain MRI segmentation, outperforming existing methods.