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Published on: December 15, 2023
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.
Background:
Choroid plexus (CP) volume has been recently proposed as a proxy for brain neuroinflammation in multiple sclerosis (MS).
Purpose:
To develop and validate a fast automatic method to segment CP using routinely acquired brain T1-weighted and FLAIR MRI.
Study Type:
Retrospective.
Population:
Fifty-five MS patients (33 relapsing-remitting, 22 progressive; mean age = 46.8 ± 10.2 years; 31 women) and 60 healthy controls (HC; mean age = 36.1 ± 12.6 years, 33 women).
Field Strength/Sequence:
3D T2-weighted FLAIR and 3D T1-weighted gradient echo sequences at 3.0 T.
Assessment:
Brain tissues were segmented on T1-weighted sequences and a Gaussian Mixture Model (GMM) was fitted to FLAIR image intensities obtained from the ventricle masks of the SIENAX. A second GMM was then applied on the thresholded and filtered ventricle mask. CP volumes were automatically determined and compared with those from manual segmentation by two raters (with 3 and 10 years' experience; reference standard). CP volumes from previously published automatic segmentation methods (freely available Freesurfer [FS] and FS-GMM) were also compared with reference standard. Expanded Disability Status Scale (EDSS) score was assessed within 3 days of MRI. Computational time was assessed for each automatic technique and manual segmentation.
Statistical Tests:
Comparisons of CP volumes with reference standard were evaluated with Bland Altman analysis. Dice similarity coefficients (DSC) were computed to assess automatic CP segmentations. Volume differences between MS and HC for each method were assessed with t-tests and correlations of CP volumes with EDSS were assessed with Pearson's correlation coefficients (R). A P value <0.05 was considered statistically significant.
Results:
Compared to manual segmentation, the proposed method had the highest segmentation accuracy (mean DSC = 0.65 ± 0.06) compared to FS (mean DSC = 0.37 ± 0.08) and FS-GMM (0.58 ± 0.06). The percentage CP volume differences relative to manual segmentation were -0.1% ± 0.23, 4.6% ± 2.5, and -0.48% ± 2 for the proposed method, FS, and FS-GMM, respectively. The Pearson's correlations between automatically obtained CP volumes and the manually obtained volumes were 0.70, 0.54, and 0.56 for the proposed method, FS, and FS-GMM, respectively. A significant correlation between CP volume and EDSS was found for the proposed automatic pipeline (R = 0.2), for FS-GMM (R = 0.3) and for manual segmentation (R = 0.4). Computational time for the proposed method (32 ± 2 minutes) was similar to the manual segmentation (20 ± 5 minutes) but <25% of the FS (120 ± 15 minutes) and FS-GMM (125 ± 15 minutes) methods.
Data Conclusion:
This study developed an accurate and easily implementable method for automatic CP segmentation in MS using T1-weighted and FLAIR MRI.
Evidence Level:
1 TECHNICAL EFFICACY: Stage 4.
Insights
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.
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.
