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Abstract #1881

Enhancing Cardiac Functional Assessment with Deep-Learning-based Automatic Segmentation of Free-Running 4D Whole-Heart MR Images

Augustin C. Ogier1, Salomé Baup1, Gorun Ilanjian1, Aisha Touray1, Angela Rocca2, Jaume Banus1, Isabel Monton Quesada1, Martin Nicoletti1, Jean-Baptiste Ledoux1,3, Jonas Richiardi1, Robert J. Holtackers1,4, Matthias Stuber1,3, Roger Hullin2, David Rotzinger1, and Ruud B. van Heeswijk1
1Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 2Cardiovascular, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, 4Radiology & Nuclear Medicine, Maastricht University Medical Centre, Maastricht, Netherlands

Synopsis

Keywords: Heart Failure, Segmentation, 5D CMR; Free-running

Motivation: Manual segmentation of 5D free-running (FR) cardiac MRI data is arduous, which hinders clinical adoption of this comprehensive imaging technique.

Goal(s): To develop and validate a deep learning (DL) framework for the automatic and accurate segmentation of isotropic whole-heart images from FR acquisitions.

Approach: We trained a 3D nnU-Net with a residual encoder using semi-automatically generated reference standard segmentations and evaluated the accuracy of the resulting fully automated segmentations through geometric and functional metrics.

Results: DL-based segmentation closely matched semi-automatic results with high Dice similarity coefficient (>0.91), low volume errors (~5%), and excellent functional parameter agreement (ICC > 0.96), confirming accuracy.

Impact: Our DL framework enables automatic, consistent segmentation of FR cardiac MRI, reducing manual workload and facilitating quantitative and comprehensive 5D assessments, supporting broader clinical and research use of advanced whole-heart imaging.

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