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...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1613320/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849467993397919744 |
|---|---|
| author | Liangdong Zhou Tracy A. Butler Xiuyuan H. Wang Samantha A. Keil Seyed Hani Hojjati Tsung-Wei Hu Mélissa Savard Firoza Z. Lussier Pedro Rosa-Neto Lidia Glodzik Mony J. de Leon Gloria C. Chiang Yi Li |
| author_facet | Liangdong Zhou Tracy A. Butler Xiuyuan H. Wang Samantha A. Keil Seyed Hani Hojjati Tsung-Wei Hu Mélissa Savard Firoza Z. Lussier Pedro Rosa-Neto Lidia Glodzik Mony J. de Leon Gloria C. Chiang Yi Li |
| author_sort | Liangdong Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c47e21cd1e1e4705a3bbe20a1c671ed5 |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-c47e21cd1e1e4705a3bbe20a1c671ed52025-08-20T03:25:59ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-07-011710.3389/fnagi.2025.16133201613320Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s diseaseLiangdong Zhou0Tracy A. Butler1Xiuyuan H. Wang2Samantha A. Keil3Seyed Hani Hojjati4Tsung-Wei Hu5Mélissa Savard6Firoza Z. Lussier7Pedro Rosa-Neto8Lidia Glodzik9Mony J. de Leon10Gloria C. Chiang11Yi Li12Department of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDouglas Hospital Research Centre, Douglas Mental Health University Institute, Montreal, QC, CanadaDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United StatesDepartments of Neurology and Neurosurgery and Psychiatry, McGill University, Montreal, QC, CanadaDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesDepartment of Radiology, Brain Health Imaging Institute, Weill Cornell Medicine, New York, NY, United StatesIntroductionThe 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.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1613320/fullchoroid plexusAlzheimer’s diseasecerebrospinal fluidneurofluidsbrain clearanceGaussian mixture model |
| spellingShingle | Liangdong Zhou Tracy A. Butler Xiuyuan H. Wang Samantha A. Keil Seyed Hani Hojjati Tsung-Wei Hu Mélissa Savard Firoza Z. Lussier Pedro Rosa-Neto Lidia Glodzik Mony J. de Leon Gloria C. Chiang Yi Li Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease Frontiers in Aging Neuroscience choroid plexus Alzheimer’s disease cerebrospinal fluid neurofluids brain clearance Gaussian mixture model |
| title | Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease |
| title_full | Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease |
| title_fullStr | Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease |
| title_full_unstemmed | Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease |
| title_short | Region-informed machine learning model for choroid plexus segmentation in Alzheimer’s disease |
| title_sort | region informed machine learning model for choroid plexus segmentation in alzheimer s disease |
| topic | choroid plexus Alzheimer’s disease cerebrospinal fluid neurofluids brain clearance Gaussian mixture model |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1613320/full |
| work_keys_str_mv | AT liangdongzhou regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT tracyabutler regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT xiuyuanhwang regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT samanthaakeil regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT seyedhanihojjati regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT tsungweihu regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT melissasavard regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT firozazlussier regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT pedrorosaneto regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT lidiaglodzik regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT monyjdeleon regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT gloriacchiang regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease AT yili regioninformedmachinelearningmodelforchoroidplexussegmentationinalzheimersdisease |