Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease
IntroductionThe choroid plexus (CP), a critical structure for cerebrospinal fluid (CSF) production, has been increasingly recognized for its involvement in Alzheimer’s disease (AD). Accurate segmentation of CP from magnetic resonance imaging (MRI) remains challenging due to its irregular shape, vari...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1613320/full |
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| Summary: | IntroductionThe choroid plexus (CP), a critical structure for cerebrospinal fluid (CSF) production, has been increasingly recognized for its involvement in Alzheimer’s disease (AD). Accurate segmentation of CP from magnetic resonance imaging (MRI) remains challenging due to its irregular shape, variable MR signal, and proximity to the lateral ventricles. This study aimed to develop and evaluate a region-informed Gaussian Mixture Model (One-GMM) for automatic CP segmentation using anatomical priors derived from FreeSurfer (FS) software and compare it with manual, FS, and one previous GMM-based (Two-GMM) methods.Materials and methodsT1-weighted (T1w) and T2-fluid-attenuated inversion recovery (FLAIR) MRI scans were acquired from 38 participants [19 cognitively normal (CN)], 11 with mild cognitive impairment (MCI), and 8 with AD. Manual segmentations served as ground truth. A GMM was applied within an anatomically constrained region combining the lateral ventricles and CP derived from FS reconstruction. The segmentation accuracy was assessed using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and volume difference percentage (VD%). Results were compared with those from FS and one previous GMM method-based segmentations across diagnostic groups.ResultsThe region-informed One-GMM achieved significantly higher accuracy compared to FS and Two-GMM, with a mean DSC of 0.82 ± 0.05 for One-GMM versus 0.24 ± 0.11 for FS (p < 0.001), and 0.66 ± 0.10 for Two-GMM (p < 0.001), lower HD95 (One-GMM: 6.06 ± 10.32 mm vs. FS: 26.21 ± 7.32 mm vs. Two-GMM: 10.58 ± 6.47 mm), and comparable volume difference (One-GMM: 20.97 ± 9.53% vs. FS: 24.32 ± 28.13% vs. Two-GMM: 24.27 ± 22.10, p = 0.87). Segmentation accuracy of our proposed method was consistent across all diagnostic groups. Clinical analysis showed that there is no diagnostic group difference in CP volume obtained from manual, FS, Two-GMM, and our proposed One-GMM methods. In the whole cohort, there are also no age and sex effects of CP volume with all methods. Restricting to the CN group, CP volume from both manual (p = 0.03), Two-GMM (p < 0.01) and the proposed One-GMM (p = 0.05), methods show an aging effect, but not for the FS segmented CP volume (p = 0.22).ConclusionA region-informed One-GMM method significantly improved CP segmentation accuracy over FS, providing a practical and accessible tool for CP quantification in AD and other research studies. Within this small cohort, no diagnostic group difference in CP volume was observed. An aging effect of CP volume was found within the CN group. |
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| ISSN: | 1663-4365 |