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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT.

Meera Srikrishna1, Joana B Pereira2, Rolf A Heckemann3

  • 1Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden; Department of Psychiatry and Neurochemistry, Institute of Physiology and Neuroscience, University of Gothenburg, Gothenburg, Sweden.

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Summary

This study introduces an automated deep learning method for segmenting brain tissues like grey matter (GM) and white matter (WM) from head CT scans. This technique enables quantitative analysis of brain structures using widely available CT imaging.

Keywords:
Brain image segmentationConvolutional neural networks (CNN)Deep learningcomputed tomography (CT)

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • Magnetic resonance (MR) imaging is standard for neuroimaging analysis, but computed tomography (CT) is more accessible and affordable.
  • CT scans are typically used for visual assessment, with limited application in quantitative brain tissue analysis due to insufficient contrast.
  • Previous methods have not effectively utilized CT for detailed brain tissue segmentation, such as grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).

Purpose of the Study:

  • To develop and validate an automatic method for segmenting GM, WM, CSF, and intracranial volume (ICV) from head CT images.
  • To demonstrate the feasibility of using deep learning for quantitative analysis of brain structures from CT data.
  • To establish CT-based segmentation as a viable alternative or complement to MR-based methods in neuroscience research and clinical practice.

Main Methods:

  • A U-Net deep learning model was employed for automated segmentation of brain tissues.
  • The model was trained and validated using CT images with segmentation labels derived from co-acquired MR images.
  • Data from 744 participants in the Gothenburg H70 Birth Cohort Studies were utilized.

Main Results:

  • The proposed U-Net model achieved high accuracy in segmenting unseen CT images, with Dice coefficients of 0.79 for GM, 0.82 for WM, 0.75 for CSF, 0.93 for brain volume, and 0.98 for ICV.
  • Benchmarks against established MR-based methods and image degradation tests confirmed the robustness of the CT-derived segmentations.
  • The results indicate that CT scans can provide reliable quantitative data for brain tissue delineation.

Conclusions:

  • Automatic segmentation of brain tissues from CT images is feasible with deep learning, overcoming previous contrast limitations.
  • This method expands the utility of CT scans for quantitative neuroimaging, offering a cost-effective alternative for research and clinical applications.
  • CT-derived brain tissue quantification opens new avenues for studying neurological conditions and brain health in large populations.