Abstract #0269

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

Lars Bielak^1,2, Nicole Wiedenmann^2,3, Arnie Berlin⁴, Leonard Hägele¹, Thomas Lottner¹, Sebastian Gross⁵, Anca-Ligia Grosu^2,3, and Michael Bock^1,2

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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