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Automatic brain extraction for rat magnetic resonance imaging data using U2-Net.

Shengxiang Liang1,2,3, Xiaolong Yin1,4, Li Huang4

  • 1National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, People's Republic of China.

Physics in Medicine and Biology
|September 2, 2023
PubMed
Summary

A new deep learning method using U²-Net significantly improves rat brain MRI skull stripping. This advanced technique offers reliable segmentation for enhanced pre-processing of rodent brain imaging data.

Keywords:
brain extractiondeep learningmagnetic resonance imagingrat brainsegmentationskull stripping

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Deep Learning

Background:

  • Skull stripping is crucial for rodent brain MRI pre-processing.
  • Accurate segmentation of intracranial tissue is essential for quantitative analysis.

Purpose of the Study:

  • To develop and evaluate a novel deep learning-based skull stripping method for rat brain MRI using U²-Net.
  • To compare the performance of the U²-Net method against traditional segmentation techniques.

Main Methods:

  • U²-Net model applied to segment 599 rat brain MRI scans.
  • Manual labeling of intracranial tissue for training and validation sets (80% train, 20% test).
  • Quantitative evaluation using Dice, Jaccard, Sensitivity, Specificity, Pixel Accuracy, and Hausdorff metrics.

Main Results:

  • U²-Net demonstrated superior performance compared to RATS and BrainSuite software.
  • Achieved high quantitative scores: Dice coefficient 0.9907 ± 0.0016, Jaccard 0.9816 ± 0.0032.
  • Excellent specificity (0.9989 ± 0.0002) and low false positive rate (0.0009 ± 0.0002).

Conclusions:

  • The U²-Net based method provides a reliable and accurate approach for rat brain MRI skull stripping.
  • This deep learning technique enhances the pre-processing pipeline for rodent neuroimaging studies.
  • Contributes a valuable tool for researchers analyzing rat brain MRI data.