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Time-resolved segmentation of the aorta from 4D flow MRI using deep learning: towards comprehensive evaluation in health and disease

12 November 2026 (09:10 - 09:55)
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Congress Presentation Part of: Decoding the heart on cardiac magnetic resonance imaging Magnetic Resonance Imaging Delegates Premium Access ESC Digital & AI Summit 2026 European Society of Cardiology

About the speaker

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Ms Nadjia Kachenoura

Sorbonne University, Paris (France)

6 More presentations in this session

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Toward clinically deployable 4D Flow CMR: a systematic review of automated segmentation and the first open haemodynamic benchmark dataset
Professor P. Lamata (London, GB)
Abstract Slides
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Deep learning based automatic temporally aligned atrial strain from four-chamber cine cardiac MRI
Miss S. Mueller (Heidelberg, DE)
Abstract Slides
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Generalizable AI for cardiac imaging: neural fields enable robust ventricular function estimation across heterogeneous cine MRI datasets
Doctor S. Ruiperez-Campillo (Zurich, CH)
Abstract Slides
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A controllable AI cardiac atlas for interpretable phenotyping using cardiovascular magnetic resonance
Doctor T. Hadler (Berlin, DE)
Abstract Slides
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Med-DualLoRA: federated adaptation of cardiac MRI foundation models under multi-center heterogeneity for cardiovascular disease detection
Mr J. Perramon Llussa (Barcelona, ES)
Abstract Slides

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Decoding the heart on cardiac magnetic resonance imaging

The Event

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ESC Digital & AI Summit 2026

12 November 2026 09:10 CET
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ESC 365 is supported by

Lilly Explore sponsored resources
novartis Explore sponsored resources
novo-nordisk Explore sponsored resources