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Abstract #2157

Reducing the impact of texture on deep-learning brain tissue segmentation networks trained with simulated MR images

Yasmina Al Khalil1, Aymen Ayaz1, 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


Deep learning-based segmentation algorithms largely rely on the availability of extensive, clinically representative data. While collecting such data requires vast resources, image simulation has the potential of generating realistic data reproducing a wide range of scanner sequences or parameters. In this work, we present an MR image simulation pipeline and evaluate its potential for training a deep-learning network for segmenting several brain structures in T1-weighted images acquired from real scanners. We additionally demonstrate how to prevent performance degradation from the lack of tissue texture in simulated images by combining statistical texture analysis and filtering on the evaluation image set.

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