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Abstract #3560

Addressing the need for less MRI sequence dependent DL-based segmentation methods: model generalization to multi-site and multi-scanner data

Yasmina Al Khalil1, 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 versatility of MRI acquisition parameters and sequences can have a substantial impact on the design and performance of medical image segmentation algorithms. Even though recent studies report excellent results of deep-learning (DL) based algorithms for tissue segmentation, their generalization capability and sequence dependence is rarely addressed, while being crucial for inclusion in clinical settings. This study attempts to demonstrate the lack of adaptation of such algorithms to unseen data from different sites and scanners. For this purpose, we use a 3D U-Net trained for brain tumor detection and test it site-wise to evaluate how well generalization can be achieved.

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