Abstract #4806

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

Nina Jacobsen¹, Andreas Deistung^1,2, Dagmar Timmann², Sophia Luise Goericke³, Jürgen R. Reichenbach^1,4, 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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.