Cargando…
Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011049/ https://www.ncbi.nlm.nih.gov/pubmed/36913908 http://dx.doi.org/10.1016/j.nicl.2023.103368 |
_version_ | 1784906300185903104 |
---|---|
author | Yazdan-Panah, Arya Schmidt-Mengin, Marius Ricigliano, Vito A.G. Soulier, Théodore Stankoff, Bruno Colliot, Olivier |
author_facet | Yazdan-Panah, Arya Schmidt-Mengin, Marius Ricigliano, Vito A.G. Soulier, Théodore Stankoff, Bruno Colliot, Olivier |
author_sort | Yazdan-Panah, Arya |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10011049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100110492023-03-15 Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis Yazdan-Panah, Arya Schmidt-Mengin, Marius Ricigliano, Vito A.G. Soulier, Théodore Stankoff, Bruno Colliot, Olivier Neuroimage Clin Regular Article 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. Elsevier 2023-03-06 /pmc/articles/PMC10011049/ /pubmed/36913908 http://dx.doi.org/10.1016/j.nicl.2023.103368 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Regular Article Yazdan-Panah, Arya Schmidt-Mengin, Marius Ricigliano, Vito A.G. Soulier, Théodore Stankoff, Bruno Colliot, Olivier Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title | Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title_full | Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title_fullStr | Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title_full_unstemmed | Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title_short | Automatic segmentation of the choroid plexuses: Method and validation in controls and patients with multiple sclerosis |
title_sort | automatic segmentation of the choroid plexuses: method and validation in controls and patients with multiple sclerosis |
topic | Regular Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10011049/ https://www.ncbi.nlm.nih.gov/pubmed/36913908 http://dx.doi.org/10.1016/j.nicl.2023.103368 |
work_keys_str_mv | AT yazdanpanaharya automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis AT schmidtmenginmarius automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis AT riciglianovitoag automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis AT souliertheodore automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis AT stankoffbruno automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis AT colliotolivier automaticsegmentationofthechoroidplexusesmethodandvalidationincontrolsandpatientswithmultiplesclerosis |