Fig. 2
Example choroid plexus segmentations from machine learning models in a 53 year old male with mild cognitive impairment. From left to right, columns show results from T1-weighted images, T2-weighted images, and T2-weighted FLAIR images. The first row (panels a–c) shows the anatomical MRI sequence utilized in this study for deep learning training, and the second row (d–f) shows these same images magnified on the lateral ventricles where the majority of the choroid plexus resides. The remaining rows show the manual segmentations (g–i), machine learning output segmentations (j–l), and the overlay of these segmentations in axial slices (m–o) and 3D renderings (p–r) for each type of MRI contrast. The 3D renderings show the manual segmentation in blue (i.e., under-segmentation), the machine learning segmentation in red (i.e., over-segmetntation, and the overlap between the two in white. The Dice scores of each model (T1-weighted: 0.78, T2-weighted: 0.78, T2-weighted FLAIR: 0.80) are shown and reflect consistently accurate performance across MRI sequences. (MRI: magnetic resonance imaging; FLAIR: FLuid-Attenuated Inversion Recovery)