Abstract #2720

The Weakest Link in the Chain: How MR Data Quality influences Convolutional Neural Network Performance

Lars Bielak¹, Hatice Bunea², Nicole Wiedenmann², Anca-Ligia Grosu², and Michael Bock¹

¹Dept. of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, ²Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany

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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