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Abstract #3519

Simulated CMR images can improve the performance and generalization capability of deep learning-based segmentation algorithms

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

The generalization capability of deep learning-based segmentation algorithms across different sites and vendors, as well as MRI data with high variance in contrast, is limited. This affects the usability of such automated segmentation algorithms in clinical settings. The lack of freely accessible medical datasets additionally limits the development of stable models. In this work, we explore the benefits of adding a simulated dataset, containing realistic contrast variance, into the training procedure of the neural network for one of the most clinically important segmentation tasks, the CMR ventricular cavity segmentation.

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