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Related Experiment Video

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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Specialized gray matter segmentation via a generative adversarial network: application on brain white matter

Mahdi Bashiri Bawil1, Mousa Shamsi1, Abolhassan Shakeri Bavil2

  • 1Biomedical Engineering Faculty, Sahand University of Technology, Tabriz, Iran.

Frontiers in Neuroscience
|October 15, 2024
PubMed
Summary

This study introduces an automated method for classifying white matter hyperintensities (WMH), particularly juxtacortical WMH (JCWMH), using only FLAIR images. The novel approach significantly improves detection accuracy and processing speed for brain disease diagnosis.

Keywords:
MRI imagesWMH classificationconditional generative adversarial networkdeep learninggray matter segmentationjuxtacortical WMHmultiple sclerosiswhite matter hyperintensities

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Artificial Intelligence in Medicine

Background:

  • White matter hyperintensities (WMH) on FLAIR MRI are key indicators in neurodegenerative diseases like Multiple Sclerosis (MS).
  • Current automated WMH classification systems are lacking, necessitating advanced detection and classification methods for improved diagnosis and monitoring.
  • Juxtacortical WMH (JCWMH) presents unique classification challenges due to difficulties in accurately segmenting gray matter (GM) masks.

Purpose of the Study:

  • To develop an automated method for specialized gray matter (GM) segmentation for improved WMH classification, especially JCWMH.
  • To create a pipeline integrating various image masks to refine GM maps and train a conditional generative adversarial network (cGAN).
  • To enable WMH classification based on distances to ventricular and GM masks, reducing reliance on multi-sequence MRI inputs.

Main Methods:

  • A novel pipeline was developed to integrate white matter, cerebrospinal fluid, ventricles, and WMH masks for refining GM maps.
  • A conditional generative adversarial network (cGAN) was trained using the pipeline to enable classification using only FLAIR images.
  • WMH classification was performed by analyzing distances between WMH and ventricular/GM masks, utilizing a manually segmented local dataset.

Main Results:

  • The proposed method achieved a Dice similarity coefficient of 0.76, precision of 0.69, and sensitivity of 0.84 for JCWMH classification.
  • It significantly outperformed the conventional approach using T1-derived GM masks, which yielded lower scores (0.66, 0.55, 0.81).
  • The automated method processed images in under one second, compared to over two minutes for traditional methods.

Conclusions:

  • The developed method offers an automated, fast, and initialization-free approach for WMH analysis exclusively using FLAIR images.
  • This innovation simplifies complexity and enhances efficiency, facilitating more accurate clinical analysis of WMH.
  • The findings pave the way for improved diagnostic and monitoring tools for brain diseases characterized by WMH.