MRF-Synth: An Image Generation Framework for Learning Contrast-Invariant Brain Segmentation

Motivation: Intensity-based brain segmentation methods face challenges with generalizability, as they are susceptible to site, age, and contrast variations. Goal(s): Develop an efficient, unified framework to train segmentation networks that are insensitive to contrast variations. Approach: MRF sequences encode image time series that include both common and uncommon contrasts. We develop MRF-Synth, a framework to generate contrasts from MRF quantitative maps for training and evaluating contrast-invariant networks. Results: We show that a segmentation U-Net trained with MRF-Synth yields highly consistent results across contrasts, vendors, and ages (DICE > 0.86 in adults). Impact: MRF-Synth represents an efficient, generalizable framework for developing and evaluating contrast-invariant segmentation networks. We demonstrate the utility of MRF-Synth in training a U-Net to segment healthy MR brain images into 18 anatomical regions regardless of contrast, scanner, vendor, or age.