Late Fusion U-Net with GAN-Based Augmentation for Generalizable Cardiac MRI Segmentation

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Accurate segmentation of the right ventricle (RV) in cardiac magnetic resonance (CMR) images is crucial for ventricular structure and function assessment. However, due to its variable anatomy and ill-defined borders, RV segmentation remains an open problem. While recent advances in deep learning show great promise in tackling these challenges, such methods are typically developed on homogeneous data-sets, not reflecting realistic clinical variation in image acquisition and pathology. In this work, we develop a model, aimed at segmenting all three cardiac structures in a multi-center, multi-disease and multi-view setting, using data provided by the M&Ms-2 challenge. We propose a pipeline addressing various aspects of segmenting heterogeneous data, consisting of heart region detection, augmentation through image synthesis and multi-fusion segmentation. Our extensive experiments demonstrate the importance of different elements of the pipeline, achieving competitive results for RV segmentation in both short-axis and long-axis MR images.

Originele taal-2Engels
TitelStatistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge
Subtitel12th International Workshop, STACOM 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Revised Selected Papers
RedacteurenEsther Puyol Antón, Mihaela Pop, Carlos Martín-Isla, Maxime Sermesant, Avan Suinesiaputra, Oscar Camara, Karim Lekadir, Alistair Young
Plaats van productieCham
Aantal pagina's14
ISBN van elektronische versie978-3-030-93722-5
ISBN van geprinte versie978-3-030-93721-8
StatusGepubliceerd - 2022
Evenement12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021 - Strasbourg, Frankrijk
Duur: 27 sep. 202127 sep. 2021

Publicatie series

NaamLecture Notes in Computer Science (LNCS)
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349
NaamImage Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)


Congres12th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2021 held in conjunction with MICCAI 2021

Bibliografische nota

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.


Acknowledgments. This research is a part of the openGTN project, supported by the European Union in the Marie Curie Innovative Training Networks (ITN) fellowship program under project No. 764465.

European Union’s Horizon Europe research and innovation programme764465
European Commission


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