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Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural

Humera Azam1, Humera Tariq2, Danish Shehzad3

  • 1Department of Computer Science, University of Karachi, Karachi 75270, Pakistan.

Brain Sciences
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated deep learning framework using Mask-RCNN for brain magnetic resonance image skull stripping. The novel method achieves higher accuracy and precision, reducing processing time and costs compared to traditional techniques.

Keywords:
MRIbrain magnetic resonance imagesdeep learningfully automated skull strippingregion-based segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Skull stripping is crucial for brain MRI analysis.
  • Traditional methods have limitations in accuracy and automation.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), offers advanced segmentation capabilities.

Purpose of the Study:

  • To develop and validate a fully automated deep learning framework for skull stripping of brain MR images.
  • To compare the proposed method's performance against traditional skull stripping tools.
  • To enhance accuracy, precision, and efficiency in brain MRI preprocessing.

Main Methods:

  • Implemented a Mask Region-based Convolutional Neural Network (Mask-RCNN) framework.
  • Trained the model from scratch using ResNet-101 and Feature Pyramid Network (FPN) backbones.
  • Validated the framework on three diverse datasets (BrainWeb, NAMIC, local hospital) with T1-weighted images.

Main Results:

  • The Mask-RCNN framework achieved a mean average precision (mAP) of 93% and a Content Validity Index (CVI) of 0.95%.
  • Outperformed traditional methods like Brain Extraction Tools (BET) and Brain Surface Extraction (BSE).
  • Demonstrated improved accuracy, precision, reduced processing time, and operational costs.

Conclusions:

  • The proposed automated Mask-RCNN framework significantly improves skull stripping for brain MR images.
  • Offers a more efficient, accurate, and cost-effective alternative to conventional methods.
  • Provides a foundation for future research in explainable artificial intelligence (XAI) for medical imaging.