Choroid Plexus Segmentation Using Optimized 3D U-Net

The choroid plexus is the primary organ that secretes the cerebrospinal fluid. Its structure and function may be associated with the brain drainage pathway and the clearance of amyloid-beta in Alzheimer's Disease. However, choroid plexus segmentation methods have rarely been studied. Therefore, the purpose of this work is to fill the gap using a deep convolutional network. MR images of 10 healthy subjects ( 75.5±8.0 years) were retrospectively selected from the Alzheimer's Disease Neuroimaging Initiative database (ADNI). The benchmark of choroid plexus segmentation was provided by the FreeSurfer package and manual correction. A 3D U-Net was developed and optimized in the patch extraction, augmentation, and loss function. In leave-one-out cross-validations, the optimized U-Net provided superior performance compared to the FreeSurfer results (Dice score 0.732±0.046 vs 0.581±0.093, Jaccard coefficient 0.579±0.057 vs 0.416±0.091, 95% Hausdorff distance 1.871±0.549 vs 7.257±5.038, and sensitivity 0.761 ±0.078 vs 0.539±0.117).