MultiFlow: A unified deep learning framework for multi-vessel classification, segmentation and clustering of phase-contrast MRI validated on a multi-site single ventricle patient cohort
Journal:
arXiv
Published Date:
Feb 17, 2025
Abstract
This study presents a unified deep learning (DL) framework, MultiFlowSeg, for
classification and segmentation of velocity-encoded phase-contrast magnetic
resonance imaging data, and MultiFlowDTC for temporal clustering of flow
phenotypes. Applied to the FORCE registry of Fontan procedure patients,
MultiFlowSeg achieved 100% classification accuracy for the aorta, SVC, and IVC,
and 94% for the LPA and RPA. It demonstrated robust segmentation with a median
Dice score of 0.91 (IQR: 0.86-0.93). The automated pipeline processed registry
data, achieving high segmentation success despite challenges like poor image
quality and dextrocardia. Temporal clustering identified five distinct patient
subgroups, with significant differences in clinical outcomes, including
ejection fraction, exercise tolerance, liver disease, and mortality. These
results demonstrate the potential of combining DL and time-varying flow data
for improved CHD prognosis and personalized care.
