Fig. 1

Overview of the processing pipeline of the anatomical magnetic resonance imaging (MRI) utilized in the proposed deep learning method. Examples are shown for a T₁-weighted MRI, but this pipeline also was utilized for T₂-weighted and T₂-weighted FLuid-Attenuated Inversion Recovery (FLAIR) MRI. Input images were registered to MNI152 space and cropped around the choroid plexus based off a probabilistic atlas generated from ground truth manual choroid plexus segmentations. Cropped images were then used as training input for the 3D U-NET fully convolutional neural network. The number of inputs for each trained model was 1 and the number of output structures was 1 (i.e., choroid plexus). Cropped outputs were then decropped and inverse transformed to the native imaging space. Example images are shown from a 69 year old male with Parkinson’s disease. (MRI: magnetic resonance imaging; Conv: convolution; ReLu: rectified linear unit; Tanh: hyperbolic tangent)