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Abstract #0269

Evaluating Noise Robustness of CNN-based Head&Neck Tumor Segmentations on Multiparametric MRI Data

Lars Bielak1,2, Nicole Wiedenmann2,3, Arnie Berlin4, Leonard H├Ągele1, Thomas Lottner1, Sebastian Gross5, Anca-Ligia Grosu2,3, and Michael Bock1,2
1Dept. of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany, 2German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany, 3Dept. of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine, Freiburg, Germany, 4The MathWorks, Inc., Novi, MI, United States, 5The MathWorks, Inc., Ismaning, Germany

Multiparametric MRI imaging in combination with PET/CT is the basis for precise tumor segmentation in radiation therapy. We trained a segmentation CNN on the multiparametric MRI data of head and neck squamous cell carcinoma patients and investigated the network robustness against noise corruption in the input channels. Overall noise robustness and differences between seven different input contrasts were compared.

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