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Abstract #1755

Late feature fusion and GAN-based augmentation for generalizable cardiac MRI segmentation

Yasmina Al Khalil1, Sina Amirrajab1, Cristian Lorenz2, Jürgen Weese2, Josien Pluim1, and Marcel Breeuwer1,3
1Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, 2Philips Research Laboratories, Hamburg, Germany, 3Philips Healthcare, MR R&D - Clinical Science, Best, Netherlands


While recent deep-learning-based approaches in automatic cardiac magnetic resonance image segmentation have shown great promise to alleviate the need for manual segmentation, most are not applicable to realistic clinical scenarios. This is largely due to training on mainly homogeneous datasets, without variation in acquisition parameters and pathology. In this work, we develop a model applicable in multi-center, multi-disease, and multi-view settings, where we combine heart region detection, augmentation through synthesis and multi-fusion segmentation to address various aspects of segmenting heterogeneous cardiac data. Our experiments demonstrate competitive results in both short-axis and long-axis MR images, without physically acquiring more training data.

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