Keywords: Segmentation, Multi-Contrast
Motivation: Recent studies have emphasized the relevance of accurate measures of the choroid plexus (ChP), which operates at the blood-cerebrospinal fluid (CSF) barrier and plays a fundamental role in CSF production, circulation, and neuro-immune surveillance.
Goal(s): To improve the existing ChP segmentation by leveraging the complementary nature of multi-contrast MRI together with a self-configuring deep-learning framework.
Approach: Multi-contrast segmentation is assessed by comparing a previous single-contrast implementation with the novel multi-contrast approach and gold-standard neuroradiologist manual segmentation.
Results: The Dice-Sørensen increased by 5.2 points using multi-contrast ChP segmentation, demonstrating that T1-weighted, T2-weighted, and T2-FLAIR can be used together to provide improved, complementary segmentation accuracy.
Impact: This study evaluates multi-contrast MRIs as inputs to a self-configuring deep learning framework to provide a new tool for segmentation of the choroid plexus, which has gained much recent interest as the most proximal structure in the neurofluid circuit.
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