In this work, tumor segmentation performance of a convolutional neural network is tested with respect to input data quality. 19 patients suffering from head and neck tumors underwent multi-parametric MRI including diffusion weighted imaging. The network was trained on multiparametric MR images with and without geometrically corrected diffusion data. With distortion correction, the Dice coefficient could be increased by 22% over uncorrected data showing the necessity for geometric image pre-processing in neural network analysis.
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