MultiFlowSeg: Unified deep learning model for multi-vessel classification and segmentation of phase-contrast MRI in single ventricle patients

Motivation: The FORCE registry of single-ventricle patients contains >5,000 MRI scans, requiring standardized segmentation of five major blood vessels for flow quantification to predict patient outcomes —a challenging task due to complex single-ventricle anatomy. Goal(s): Automate flow quantification for the left and right pulmonary arteries, aorta, and superior and inferior vena cavae. Approach: Develop a unified deep learning model and automated pipeline that extracts phase-contrast flow planes from the registry and performs simultaneous classification and segmentation of the five vessels. Results: MultiFlowSeg achieved a median Dice score of 0.91 on 50 test sets and 89% segmentation success across 630 exams processed through the pipeline. Impact: We have developed a pipeline that uses our novel MultiFlowSeg model to automate the extraction, classification, and segmentation of phase-contrast MRI images. It enables rapid, accurate flow quantification for five blood vessels in a single ventricle registry without manual input.