Abstract #0619

Comparison of Image Normalization Techniques for Rectal Cancer Segmentation in Multi-Center Data: Initial results

Steffen Albert¹, Barbara D. Wichtmann², Wenzhao Zhao³, Jürgen Hesser³, Ulrike I. Attenberger², Lothar R. Schad¹, and Frank G. Zöllner¹

¹Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ²Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany, ³Data Analysis and Modeling in Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

Synopsis

We evaluated the influence of normalization (setting mean and standard deviation, histogram matching and percentiles) on the segmentation of rectal cancer on multimodal images when operating on multicenter data as part of a Radiomics pipeline. We used two different networks for segmentation. When training and evaluating on all data or data from a single center, normalization did not play a significant role. In contrast, when training on one center and evaluating on all others, it did play a major role. Best results are obtained by normalization using percentiles. Fixing the mean and standard deviation did not work well.

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