Al Khalil, Yasmina

Amirrajab, Sina

Breeuwer, Marcel

Pluim, Josien

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&amp;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.</p

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

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