Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis
Open accessAbstract
Choroid Plexuses (ChP) are structures located in the ventricles that produce the cerebrospinal fluid (CSF) in the central nervous system. They are also a key component of the blood-CSF barrier. Recent studies have described clinically relevant ChP volumetric changes in several neurological diseases including Alzheimer's, Parkinson's disease, and multiple sclerosis (MS). Therefore, a reliable and automated tool for ChP segmentation on images derived from magnetic resonance imaging (MRI) is a crucial need for large studies attempting to elucidate their role in neurological disorders. Here, we propose a novel automatic method for ChP segmentation in large imaging datasets. The approach is based on a 2-step 3D U-Net to keep preprocessing steps to a minimum for ease of use and to lower memory requirements. The models are trained and validated on a first research cohort including people with MS and healthy subjects. A second validation is also performed on a cohort of pre-symptomatic MS patients having acquired MRIs in routine clinical practice. Our method reaches an average Dice coefficient of 0.72 ± 0.01 with the ground truth and a volume correlation of 0.86 on the first cohort while outperforming FreeSurfer and FastSurfer-based ChP segmentations. On the dataset originating from clinical practice, the method reaches a Dice coefficient of 0.67 ± 0.01 (being close to the inter-rater agreement of 0.64 ± 0.02) and a volume correlation of 0.84. These results demonstrate that this is a suitable and robust method for the segmentation of the ChP both on research and clinical datasets.
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The segmentation model used in this study was previously published and segments the choroid plexuses of the brain from T1-weighted MRI.
“For this study, we utilized a previously published segmentation model that segments the choroid plexuses of the brain T1-weighted (T1w) from magnetic resonance imaging (MRI) scans 22”
From Automatic quality control of segmentation results using early epochs as data augmentation: application to choroid plexuses (2024) · cites this paper as [22]
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- Authors
- Yazdan-Panah, A., Schmidt-Mengin, M., Ricigliano, V. A. G., Soulier, T., Stankoff, B., & Colliot, O.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Colliot, Olivier
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 38
- Pages
- 103368
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC10011049
- OpenAlex
- W4323317043
- Cited by
- 58
- References
- 34
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 29 · 2023: 3 · 2022: 1
- FWCI
- 5.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agence nationale de la recherche
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- multiple sclerosis, cerebrospinal fluid, 3D U-Net, brain MRI, choroid plexus volume, blood-CSF barrier, automated image segmentation, FreeSurfer comparison
- MeSH terms
- Choroid, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Multiple Sclerosis, Parkinson Disease
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