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Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture.

Bumshik Lee1, Nagaraj Yamanakkanavar1, Jae Young Choi2

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

Plos One
|August 4, 2020
PubMed
Summary
This summary is machine-generated.

A novel patch-wise U-net architecture improves brain magnetic resonance imaging (MRI) segmentation accuracy. This deep learning method enhances local information retention, achieving superior results for brain structure quantification.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Accurate brain magnetic resonance imaging (MRI) segmentation is crucial for quantifying structural changes.
  • Deep learning, particularly the U-net architecture, shows significant promise in biomedical image segmentation.

Purpose of the Study:

  • To propose a patch-wise U-net architecture for automated brain structure segmentation in structural MRI.
  • To enhance local information retention compared to conventional U-net models.

Main Methods:

  • Dividing MRI slices into non-overlapping patches.
  • Training a U-net model using these patches and corresponding ground truth patches.
  • Implementing a non-overlapping patch-wise U-net approach.

Main Results:

  • The proposed patch-wise U-net achieved an average Dice Similarity Coefficient (DSC) score of 0.93.
  • Outperformed conventional U-net by 3% and SegNet-based methods by 10% on OASIS and IBSR datasets.

Conclusions:

  • The patch-wise U-net architecture effectively segments brain structures in MRI.
  • This method offers improved accuracy and local information preservation for brain image analysis.