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Abstract #4806

Can Intensity Augmentation Improve Generalizability of CNN-based Image Segmentation?

Nina Jacobsen1, Andreas Deistung1,2, Dagmar Timmann2, Sophia Luise Goericke3, Jürgen R. Reichenbach1,4, and Daniel Güllmar1

1Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany, 2Department of Neurology, Essen University Hospital, University of Duisburg-Essen, Essen, Germany, 3Department of Diagnostic and Interventional Radiology and Neuroradiology, University of Duisburg-Essen, Essen, Germany, 4Michael-Stifel-Center-Jena for Data-Driven and Simulation Science, Friedrich-Schiller University Jena, Jena, Germany

As a strategy to achieve higher generalizability of a convolutional neural network (CNN), data augmentation may be used to introduce a higher degree of variability within the training sample. In this study, five different intensity augmentation strategies were compared and analyzed by means of the CNN segmentation performance. The results indicate how intensity augmentation improves the robustness, and thereby the generalizability, of the CNN but in some cases also compromise the segmentation performance in terms of accuracy.

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