ORIGINAL RESEARCH article
Front. Aging Neurosci.
Sec. Alzheimer's Disease and Related Dementias
Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1613320
Region-Informed Machine Learning Model for Choroid Plexus Segmentation in Alzheimer's Disease
Provisionally accepted
Liangdong Zhou1*
Tracy Butler1
Xiuyuan Wang1
Samantha A Keil1
Seyed Hani Hojjati1
Tsung-Wei Hu1Mélissa Savard2Firoza Z Lussier3
Pedro Rosa-Neto MD PhD4
Lidia Glodzik1
Mony John de Leon1
Gloria Chiang1
Yi Li1*- 1Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA, New York, United States
- 2Douglas Mental Health University Institute, Montreal, Quebec, Canada
- 3Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA, Pittsburgh, United States
- 4Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The 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 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) and compare it with manual, FS, and and one previous GMM-based (Two-GMM) methods.Materials and Methods: T1-weighted and T2-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 Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Volume Difference Percentage (VD%). Results were compared with FS and one previous GMM method-based segmentations across diagnostic groups.The 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),
Keywords: Choroid Plexus, Alzheimer's disease, Cerebrospinal Fluid, neurofluids, Brain clearance, Gaussian mixture model, FreeSurfer
Received: 17 Apr 2025; Accepted: 02 Jul 2025.
Copyright: © 2025 Zhou, Butler, Wang, Keil, Hojjati, Hu, Savard, Lussier, Rosa-Neto MD PhD, Glodzik, de Leon, Chiang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Liangdong Zhou, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA, New York, United States
Yi Li, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, NY, USA, New York, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.