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Abstract #0260

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

Tina Yao1, Nicole St. Clair2, Gabriel F Miller2, Ana Zoubian2, Jennifer A Steeden1, Rahul H Rathod2, and Vivek Muthurangu1
1Institute of Cardiovascular Science, University College London, London, United Kingdom, 2Department of Cardiology, Boston Children's Hospital, Boston, MA, United States

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence

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.

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Keywords