Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan.

Background: The choroid plexus functions as the blood-cerebrospinal fluid barrier, plays an important role in neurofluid production and circulation, and has gained increased attention in light of the recent elucidation of neurofluid circulation dysfunction in neurodegenerative conditions. However, methods for routinely quantifying choroid plexus volume are suboptimal and require technical improvements and validation. Here, we propose three deep learning models that can segment the choroid plexus from commonly-acquired anatomical MRI data and report performance metrics and changes across the adult lifespan. Methods: Fully convolutional neural networks were trained from 3-D T Results: Deep learning results yielded Dice coefficient=0.72, Hausdorff distance=1.97 mm, AUC=0.87 for T Conclusions: Findings support comparable performance in choroid plexus delineation between standard, clinically available, non-contrasted anatomical MRI sequences. The software embedding the evaluated models is freely available online and should provide a useful tool for the growing number of studies that desire to quantitatively evaluate choroid plexus structure and function (https://github.com/hettk/chp_seg).