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 clinical, 2023)
•The importance of choroid plexuses in neurological diseases has been increasingly recognized.•Reliable and well-validated methods for choroid plexus segmentation are lacking.•We propose an automatic tool to segment the choroid plexus with high accuracy.•The validation included both research and clinical datasets and normal and enlarged choroid plexuses.•The method opens new perspectives for the study of choroid plexuses on large databases. 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.