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A Fully Automatic Method to Segment Choroid Plexuses in Multiple Sclerosis Using Conventional MRI Sequences.

Loredana Storelli1, Elisabetta Pagani1, Martina Rubin1,2

  • 1Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.

Journal of Magnetic Resonance Imaging : JMRI
|August 2, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a fast, automatic method to segment choroid plexus (CP) volume in multiple sclerosis (MS) patients using MRI. The developed technique accurately measures CP volume, correlating with disease severity and offering a new tool for neuroinflammation assessment.

Keywords:
automatic segmentationchoroid plexusesinflammationmultiple sclerosis

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Area of Science:

  • Neuroimaging
  • Radiology
  • Biomedical Engineering

Background:

  • Choroid plexus (CP) volume is a potential biomarker for neuroinflammation in multiple sclerosis (MS).
  • Accurate and efficient methods for CP volume measurement are needed for clinical application.

Purpose of the Study:

  • To develop and validate a rapid, automated method for segmenting CP using standard T1-weighted and FLAIR MRI sequences.
  • To compare the performance of the proposed method against manual segmentation and existing automated techniques.

Main Methods:

  • A retrospective study involving 55 MS patients and 60 healthy controls (HC).
  • Utilized 3.0T 3D T1-weighted and FLAIR MRI sequences.
  • Developed an automated segmentation algorithm using Gaussian Mixture Models (GMM) and compared it with manual segmentation and Freesurfer (FS/FS-GMM).

Main Results:

  • The proposed automatic method achieved high segmentation accuracy (DSC=0.65) compared to FS (DSC=0.37) and FS-GMM (DSC=0.58).
  • Demonstrated strong correlation with manual segmentation volumes (R=0.70).
  • Showed significant correlations between CP volume and Expanded Disability Status Scale (EDSS) scores in MS patients (R=0.2).
  • The proposed method's computational time was significantly faster than FS and FS-GMM.

Conclusions:

  • An accurate, fast, and easily implementable automated method for CP segmentation in MS using T1-weighted and FLAIR MRI was successfully developed.
  • This automated approach holds promise for routine clinical use in assessing neuroinflammation in MS.