  Choroid plexuses (CP) are structures of the ventricles of the brain which
produce most of the cerebrospinal fluid (CSF). Several postmortem and in vivo
studies have pointed towards their role in the inflammatory process in multiple
sclerosis (MS). Automatic segmentation of CP from MRI thus has high value for
studying their characteristics in large cohorts of patients. To the best of our
knowledge, the only freely available tool for CP segmentation is FreeSurfer but
its accuracy for this specific structure is poor. In this paper, we propose to
automatically segment CP from non-contrast enhanced T1-weighted MRI. To that
end, we introduce a new model called "Axial-MLP" based on an assembly of Axial
multi-layer perceptrons (MLPs). This is inspired by recent works which showed
that the self-attention layers of Transformers can be replaced with MLPs. This
approach is systematically compared with a standard 3D U-Net, nnU-Net,
Freesurfer and FastSurfer. For our experiments, we make use of a dataset of 141
subjects (44 controls and 97 patients with MS). We show that all the tested
deep learning (DL) methods outperform FreeSurfer (Dice around 0.7 for DL vs
0.33 for FreeSurfer). Axial-MLP is competitive with U-Nets even though it is
slightly less accurate. The conclusions of our paper are two-fold: 1) the
studied deep learning methods could be useful tools to study CP in large
cohorts of MS patients; 2)~Axial-MLP is a potentially viable alternative to
convolutional neural networks for such tasks, although it could benefit from
further improvements.
