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3D segmentation framework for whole-brain segmentation from thick-slice brain MRI.

Shaofeng Jiang1, Liangli Xiong1, Zhen Chen2

  • 1Department of Biomedical Engineering, Nanchang Hangkong University, Nanchang 330063, China; Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China.

Journal of Neuroscience Methods
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel framework using bi-directional super-resolution reconstruction (BSR) to improve 3D brain MRI segmentation. The method enhances segmentation accuracy on thick-slice images by recovering missing anatomical details.

Area of Science:

  • Medical Image Analysis
  • Neuroimaging
  • Artificial Intelligence in Medicine

Background:

  • Accurate whole-brain segmentation is crucial for medical image analysis.
  • Existing 3D segmentation methods struggle with thick-slice MRI due to poor through-plane resolution and lost inter-slice information.

Purpose of the Study:

  • To develop an effective 3D segmentation framework for thick-slice brain MRI.
  • To improve the accuracy and robustness of whole-brain segmentation on low-resolution MRI data.

Main Methods:

  • A bi-directional super-resolution reconstruction (BSR)-assisted 3D segmentation framework was developed.
  • A general whole-brain segmentation model (nnU-Net) was trained on thin-slice MRI data.
  • The BSR network reconstructed missing inter-slice information to create thin-slice-like images, enhancing anatomical continuity for segmentation.
Keywords:
Cross-dataThick-slice ThicknessWhole brain segmentationnnU-Net

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Main Results:

  • The framework demonstrated improved whole-brain segmentation performance on thick-slice MRI datasets (IBSR18, LPBA40, OASIS).
  • Achieved a 3.6 percentage point improvement over nnU-Net on the IBSR18 thick-slice dataset when trained on LPBA40.
  • Obtained a 1.2 percentage point accuracy improvement on the OASIS-3 thick-slice dataset.

Conclusions:

  • The BSR-assisted framework offers an effective solution for whole-brain segmentation from thick-slice MRI.
  • This approach can facilitate more reliable brain tissue analysis in clinical settings.